A new growing pruning deep learning neural network algorithm (GP-DLNN)
During the last decade, a significant research progress has been drawn in both the theoretical aspects and the applications of Deep Learning Neural Networks. Besides their spectacular applications, optimal architectures of these neural networks may speed up the learning process and exhibit better ge...
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| Vydané v: | Neural computing & applications Ročník 32; číslo 24; s. 18143 - 18159 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Springer London
01.12.2020
Springer Nature B.V Springer Verlag |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | During the last decade, a significant research progress has been drawn in both the theoretical aspects and the applications of Deep Learning Neural Networks. Besides their spectacular applications, optimal architectures of these neural networks may speed up the learning process and exhibit better generalization results. So far, many growing and pruning algorithms have been proposed by many researchers to deal with the optimization of standard Feedforward Neural Network architectures. However, applying both the growing and the pruning on the same net may lead a good model for a big data set and hence good selection results. This work is devoted to propose a new Growing and pruning Learning algorithm for Deep Neural Networks. This new algorithm is presented and applied on diverse medical data sets. It is shown that this algorithm outperforms various other artificial intelligent techniques in terms of accuracy and simplicity of the resulting architecture. |
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| AbstractList | During the last decade, a significant research progress has been drawn in both the theoretical aspects and the applications of Deep Learning Neural Networks. Besides their spectacular applications, optimal architectures of these neural networks may speed up the learning process and exhibit better generalization results. So far, many growing and pruning algorithms have been proposed by many researchers to deal with the optimization of standard Feedforward Neural Network architectures. However, applying both the growing and the pruning on the same net may lead a good model for a big data set and hence good selection results. This work is devoted to propose a new Growing and pruning Learning algorithm for Deep Neural Networks. This new algorithm is presented and applied on diverse medical data sets. It is shown that this algorithm outperforms various other artificial intelligent techniques in terms of accuracy and simplicity of the resulting architecture. |
| Author | Omri, Nabil Zerhouni, Noureddine Fnaiech, Farhat Zemouri, Ryad Fnaiech, Nader |
| Author_xml | – sequence: 1 givenname: Ryad orcidid: 0000-0002-3283-9391 surname: Zemouri fullname: Zemouri, Ryad email: ryad.zemouri@cnam.fr organization: Cedric-Lab, CNAM, HESAM Université – sequence: 2 givenname: Nabil surname: Omri fullname: Omri, Nabil organization: FEMTO-ST, University of Bourgogne-Franche-Comté – sequence: 3 givenname: Farhat surname: Fnaiech fullname: Fnaiech, Farhat organization: ENSIT, LR13ES03 SIME, Université de Tunis – sequence: 4 givenname: Noureddine surname: Zerhouni fullname: Zerhouni, Noureddine organization: FEMTO-ST, University of Bourgogne-Franche-Comté – sequence: 5 givenname: Nader surname: Fnaiech fullname: Fnaiech, Nader organization: ISIMa- Institut supérieur d’informatique de Mahdia |
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| Cites_doi | 10.1109/MECBME.2018.8402426 10.1109/TPAMI.2013.50 10.1109/72.248452 10.1109/TMI.2016.2553401 10.1109/TNNLS.2011.2178124 10.1109/ICICIP.2010.5564272 10.1002/int.4550080406 10.1007/s00521-011-0585-7 10.1109/TNNLS.2018.2790388 10.1016/j.asoc.2008.01.013 10.1016/j.amc.2008.05.049 10.1109/WSOM.2017.8020002 10.1109/ISCCSP.2004.1296579 10.1109/72.572102 10.1007/s11063-011-9196-7 10.1016/j.neucom.2005.04.010 10.1016/j.compbiomed.2017.04.012 10.1016/j.eswa.2016.05.022 10.1109/TFUZZ.2010.2070841 10.1162/neco.1997.9.1.185 10.1109/TNN.2006.878121 10.1109/TNNLS.2014.2350957 10.1007/s10462-016-9526-2 10.1109/IJCNN.2008.4634268 10.1016/j.neunet.2014.12.001 10.1007/s00521-013-1367-1 10.1109/TNN.2008.2005604 10.1016/j.compmedimag.2016.07.004 10.1016/0925-2312(94)90055-8 10.1109/72.963775 10.1016/j.neunet.2014.09.003 10.1016/j.media.2016.07.007 10.1007/s10489-016-0767-1 10.1016/j.neucom.2017.02.038 10.1016/j.neucom.2012.07.023 10.1016/j.neunet.2011.10.003 10.1109/ICNN.1993.298572 10.1109/HSI.2008.4581409 10.1109/TSMCB.2009.2021849 10.1109/72.774273 10.1016/j.neucom.2005.11.005 10.1016/j.neunet.2016.10.011 10.1016/j.neucom.2016.01.034 10.1016/j.neucom.2008.04.004 10.1109/IWACI.2011.6160018 10.1016/j.compmedimag.2017.12.001 10.1016/j.neucom.2011.05.025 10.1109/TNN.2003.813832 10.1109/IJCNN.2003.1223407 10.1109/TNN.2009.2036259 10.1109/ICCIAutom.2011.6356672 10.1109/TNNLS.2015.2479223 10.1109/72.572092 10.1109/IJCNN.2016.7727519 10.1073/pnas.87.23.9193 10.1109/TNNLS.2017.2716952 10.1109/ACCESS.2016.2624938 10.1109/TNNLS.2011.2178315 10.1016/j.media.2017.01.009 10.1109/TKDE.2008.239 10.1109/TMI.2016.2528120 10.1109/TSMCB.2004.834428 10.1109/IJCNN.2002.1007808 10.1016/j.neucom.2015.12.003 10.1038/nature14539 10.1109/72.80290 10.1109/72.623214 10.1016/j.neucom.2007.09.016 10.1016/j.ymssp.2015.10.025 10.1038/s41598-017-03405-5 10.1109/TNN.2006.871707 10.1109/72.839013 10.1016/j.neucom.2007.08.026 10.1109/TSMCB.2008.2008724 10.1109/ICNN.1988.23864 10.1016/j.neucom.2010.05.022 10.1038/s41598-017-04075-z 10.1016/j.image.2016.05.007 |
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| Issue | 24 |
| Keywords | Deep learning Constructive neural networks Deep neural networks Growing algorithm Pruning algorithm |
| Language | English |
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| References | CastellanoGFanelliAMPelilloMAn iterative pruning algorithm for feedforward neural networksIEEE Trans Neural Netw199783519531 Ai F (2011) A new pruning algorithm for feedforward neural networks. In: The fourth international workshop on advanced computational intelligence, pp 286–289 XuJLuoXWangGGilmoreHMadabhushiAA deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological imagesNeurocomputing2016191214223 AugastaMGKathirvalavakumarTA novel pruning algorithm for optimizing feedforward neural network of classification problemsNeural Process Lett2011343241 IslamMMSattarMAAminMFYaoXMuraseKA new adaptive merging and growing algorithm for designing artificial neural networksIEEE Trans Syst Man Cybern Part B (Cybern)2009393705722 ZhangRLanYHuangGBXuZBUniversal approximation of extreme learning machine with adaptive growth of hidden nodesIEEE Trans Neural Netw Learn Syst2012232365371 ReedRPruning algorithms—a surveyIEEE Trans Neural Netw199345740747 EngelbrechtAPA new pruning heuristic based on variance analysis of sensitivity informationIEEE Trans Neural Netw200112613861399 FarisHAljarahIMirjaliliSTraining feedforward neural networks using multi-verse optimizer for binary classification problemsAppl Intell2016452322332 CireşanDCGiustiAGambardellaLMSchmidhuberJMitosis detection in breast cancer histology images with deep neural networks2013BerlinSpringer411418 Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image classification using convolutional neural networks. In: 2016 international joint conference on neural networks (IJCNN), pp 2560–2567 WahabNKhanALeeYSTwo-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detectionComput Biol Med2017858697 YaoXA review of evolutionary artificial neural networksInt J Intell Syst199384539567 FnaiechNFnaiechFJervisBCherietMThe combined statistical stepwise and iterative neural network pruning algorithmIntell Autom Soft Comput2009154573589 Sun W, Tseng TLB, Zhang J, Qian W (2017) Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Graph 57, 4–9. Recent developments in machine learning for medical imaging applications van der MaatenLHintonGVisualizing data using t-sneJ Mach Learn Res2008911257926051225.68219 DuffnerSGarciaCAn online backpropagation algorithm with validation error-based adaptive learning rate2007BerlinSpringer249258 Qiao J, Zhang Y, Han H (2008) Fast unit pruning algorithm for feedforward neural network design. Appl Math Comput 205(2):622 – 627. Special issue on advanced intelligent computing theory and methodology in applied mathematics and computation AlbarqouniSBaurCAchillesFBelagiannisVDemirciSNavabNAggnet: deep learning from crowds for mitosis detection in breast cancer histology imagesIEEE Trans Med Imaging201635513131321 SetionoRA penalty-function approach for pruning feedforward neural networksNeural Comput1997911852040872.68154 ChenCLPLiuZBroad learning system: an effective and efficient incremental learning system without the need for deep architectureIEEE Trans Neural Netw Learn Syst201829110243746789 HanHQiaoJA self-organizing fuzzy neural network based on a growing-and-pruning algorithmIEEE Trans Fuzzy Syst201018611291143 Jacek C, Zarzycki H (2003) Application of rough sets in the presumptive diagnosis of urinary system diseases. In: Artificial intelligence and security in computing systems, ACS’2002 9th international conference proceedings. Kluwer Academic Publishers, pp 41–51 SubiratsJLFrancoLJerezJMC-mantec: a novel constructive neural network algorithm incorporating competition between neuronsNeural Netw201226130140 TomèDMontiFBaroffioLBondiLTagliasacchiMTubaroSDeep convolutional neural networks for pedestrian detectionSignal Process Image Commun201647482489 Goodfellow IJ, Warde-farley D, Mirza M, Courville A, Bengio Y (2013) Maxout networks. In: In ICML HanHGZhangSQiaoJFAn adaptive growing and pruning algorithm for designing recurrent neural networkNeurocomputing20172425162 Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Gordon GJ, Dunson DB (eds) Proceedings of the fourteenth international conference on artificial intelligence and statistics (AISTATS-11), vol 15, Journal of machine learning research—workshop and conference proceedings, pp 315–323 ZhangRXuZBHuangGBWangDGlobal convergence of online bp training with dynamic learning rateIEEE Trans Neural Netw Learn Syst2012232330341 Montana DJ, Davis L (1989) Training feedforward neural networks using genetic algorithms. In: Proceedings of the 11th international joint conference on artificial intelligence-volume 1, IJCAI’89. Morgan Kaufmann Publishers Inc., San Francisco, pp 762–767 LauretPFockEMaraTAA node pruning algorithm based on a Fourier amplitude sensitivity test methodIEEE Trans Neural Netw2006172273293 ShresthaSBSongQRobust learning in spikepropNeural Netw20178654681428.68254 BengioYCourvilleAVincentPRepresentation learning: a review and new perspectivesIEEE Trans Pattern Anal Mach Intell201335817981828 Lichman M (2013) UCI machine learning repository Zeiler MD (2012) Adadelta: an adaptive learning rate method. abs/1212.5701 IslamMMSattarMAAminMFYaoXMuraseKA new constructive algorithm for architectural and functional adaptation of artificial neural networksIEEE Trans Syst Man Cybern Part B (Cybern)200939615901605 Zhang Z, Qiao J (2010) A node pruning algorithm for feedforward neural network based on neural complexity. In: 2010 international conference on intelligent control and information processing, pp 406–410 FrancoLJerezJMConstructive neural networks2009Berlin, HeidelbergSpringer MicheYSorjamaaABasPSimulaOJuttenCLendasseAOp-elm: optimally pruned extreme learning machineIEEE Trans Neural Netw2010211158162 WolbergWHMangasarianOMultisurface method of pattern separation for medical diagnosis applied to breast cytologyProc Natl Acad Sci USA199087919391960709.92537 GreenspanHvan GinnekenBSummersRMGuest editorial deep learning in medical imaging: overview and future promise of an exciting new techniqueIEEE Trans Med Imaging201635511531159 XingHJHuBGTwo-phase construction of multilayer perceptrons using information theoryIEEE Trans Neural Netw2009204715721 Sietsma J, Dow RJF (1988) Neural net pruning-why and how. In: IEEE 1988 international conference on neural networks, vol 1, pp 325–333 HanHGQiaoJFA structure optimisation algorithm for feedforward neural network constructionNeurocomputing201399347357 ShresthaSBSongQAdaptive learning rate of spikeprop based on weight convergence analysisNeural Netw2015631851981325.68197 ParekhRYangJHonavarVConstructive neural-network learning algorithms for pattern classificationIEEE Trans Neural Netw2000112436451 PonnapalliPVSHoKCThomsonMA formal selection and pruning algorithm for feedforward artificial neural network optimizationIEEE Trans Neural Netw1999104964968 QiaoJLiFHanHLiWConstructive algorithm for fully connected cascade feedforward neural networksNeurocomputing2016182154164 Sabo D, Yu XH (2008) A new pruning algorithm for neural network dimension analysis. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), pp 3313–3318 WanWMabuSShimadaKHirasawaKHuJEnhancing the generalization ability of neural networks through controlling the hidden layersAppl Soft Comput200991404414 AugastaMGKathirvalavakumarTPruning algorithms of neural networks—a comparative studyCent Eur J Comput Sci201333105115 WuXRóżyckiPWilamowskiBMA hybrid constructive algorithm for single-layer feedforward networks learningIEEE Trans Neural Netw Learn Syst2015268165916683454963 MahmudMKaiserMHussainAVassanelliSApplications of deep learning and reinforcement learning to biological dataIEEE Trans Neural Netw Learn Syst201829206320793811399 XuJHoDWA new training and pruning algorithm based on node dependence and Jacobian rank deficiencyNeurocomputing2006701544558 WangGA perspective on deep imagingIEEE Access2016489148924 Nielsen AB, Hansen LK (2008) Structure learning by pruning in independent component analysis. Neurocomputing 71(10):2281–2290. Neurocomputing for vision research advances in blind signal processing KwokTYYeungDYObjective functions for training new hidden units in constructive neural networksIEEE Trans Neural Netw19978511311148 NayakJNaikBBeheraHA novel nature inspired firefly algorithm with higher order neural network: performance analysisInt J Eng Sci Technol2016191197211 HanZWeiBZhengYYinYLiKLiSBreast cancer multi-classification from histopathological images with structured deep learning modelSci Rep2017714172 SridharSSPonnavaikkoMA novel constructive neural network architecture based on improved adaptive learning strategy for pattern classification2012BerlinSpringer423433 Thivierge JP, Rivest F, Shultz TR (2003) A dual-phase technique for pruning constructive networks. In: Proceedings of the international joint conference on neural networks, 2003, vol 1, pp 559–564 YangXSDebSCuckoo search: recent advances and applicationsNeural Comput Appl2014241169174 Rifai S, Vincent P, Muller X, Glorot X, Bengio Y (2011) Contractive auto-en- coders: explicit invariance during feature extraction. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 833–840 Zemouri R, Omri N, Devalland C, Arnould L, Morello B, Zerhouni N, Fnaiech F (2018) Breast cancer diagnosis based on joint variable selection and constructive deep neural network. In: 4th IEEE middle east conference on biomedical engineering (MECBME 2018) Zeraatkar E, Soltani M, Karimaghaee P (2011) A fast convergence algorithm for bpnn based on optimal control theory based learning rate. In: The 2nd international conference on control, instrumentation and automation, pp 292–297 Puma-Villanueva WJ, dos Santos EP, Zuben FJV (2012) A constructive algorithm to synthesize arbitrarily connected feedforward neural networks. Neurocomputing 75(1), 14–32. B H Yu (4196_CR90) 2018; 99 J Xu (4196_CR86) 2006; 70 S Duffner (4196_CR13) 2007 4196_CR1 E Hosseini-Asl (4196_CR30) 2016; 27 GB Huang (4196_CR31) 2004; 34 MM Islam (4196_CR34) 2009; 39 X Yao (4196_CR89) 1993; 8 P Vincent (4196_CR79) 2010; 11 4196_CR33 DC Cireşan (4196_CR11) 2013 4196_CR37 R Zemouri (4196_CR94) 2012; 21 MM Islam (4196_CR35) 2009; 39 D Tomè (4196_CR78) 2016; 47 AP Engelbrecht (4196_CR14) 2001; 12 M Mahmud (4196_CR48) 2018; 29 4196_CR42 B Pérez-Sánchez (4196_CR57) 2016; 49 4196_CR46 MM Islam (4196_CR36) 2003; 14 T Kooi (4196_CR39) 2017; 35 4196_CR49 SC Huang (4196_CR32) 1991; 2 MG Augasta (4196_CR3) 2011; 34 M Saha (4196_CR65) 2017; 7 M Saha (4196_CR66) 2018; 64 SB Shrestha (4196_CR69) 2015; 63 W Wan (4196_CR81) 2009; 9 J Xu (4196_CR87) 2016; 191 R Setiono (4196_CR68) 1997; 9 4196_CR93 4196_CR92 4196_CR91 4196_CR10 4196_CR96 4196_CR95 F Jia (4196_CR38) 2016; 72–73 4196_CR99 4196_CR18 WH Wolberg (4196_CR83) 1990; 87 4196_CR16 TY Kwok (4196_CR41) 1997; 8 P Lauret (4196_CR43) 2006; 17 R Zhang (4196_CR97) 2012; 23 N Fnaiech (4196_CR17) 2009; 15 HJ Xing (4196_CR85) 2009; 20 N Dhungel (4196_CR12) 2017; 37 4196_CR21 4196_CR20 H He (4196_CR29) 2009; 21 (4196_CR19) 2009 HG Han (4196_CR25) 2013; 99 4196_CR28 G Wang (4196_CR82) 2016; 4 S Albarqouni (4196_CR2) 2016; 35 G Castellano (4196_CR7) 1997; 8 R Zhang (4196_CR98) 2012; 23 CLP Chen (4196_CR9) 2018; 29 L van der Maaten (4196_CR47) 2008; 9 B Chandra (4196_CR8) 2016; 63 4196_CR72 X Wu (4196_CR84) 2015; 26 4196_CR71 4196_CR76 R Reed (4196_CR62) 1993; 4 4196_CR73 4196_CR77 TY Kwok (4196_CR40) 1997; 8 H Faris (4196_CR15) 2016; 45 JL Subirats (4196_CR75) 2012; 26 H Han (4196_CR24) 2010; 18 Y LeCun (4196_CR44) 2015; 521 PVS Ponnapalli (4196_CR58) 1999; 10 Y Miche (4196_CR50) 2010; 21 XS Yang (4196_CR88) 2014; 24 L Behera (4196_CR5) 2006; 17 Y LeCun (4196_CR45) 1990 R Parekh (4196_CR55) 2000; 11 SS Sridhar (4196_CR74) 2012 J Schmidhuber (4196_CR67) 2015; 61 HG Han (4196_CR26) 2017; 242 H Greenspan (4196_CR22) 2016; 35 4196_CR54 4196_CR52 4196_CR51 Z Han (4196_CR27) 2017; 7 4196_CR56 4196_CR59 J Nayak (4196_CR53) 2016; 19 SB Shrestha (4196_CR70) 2017; 86 4196_CR61 4196_CR64 N Wahab (4196_CR80) 2017; 85 4196_CR63 MG Augasta (4196_CR4) 2013; 3 J Qiao (4196_CR60) 2016; 182 Y Bengio (4196_CR6) 2013; 35 M Hagiwara (4196_CR23) 1994; 6 |
| References_xml | – reference: Thivierge JP, Rivest F, Shultz TR (2003) A dual-phase technique for pruning constructive networks. In: Proceedings of the international joint conference on neural networks, 2003, vol 1, pp 559–564 – reference: KwokTYYeungDYConstructive algorithms for structure learning in feedforward neural networks for regression problemsIEEE Trans Neural Netw199783630645 – reference: Zhang Z, Qiao J (2010) A node pruning algorithm for feedforward neural network based on neural complexity. In: 2010 international conference on intelligent control and information processing, pp 406–410 – reference: KwokTYYeungDYObjective functions for training new hidden units in constructive neural networksIEEE Trans Neural Netw19978511311148 – reference: Lan Y, Soh YC, Huang GB (2010) Constructive hidden nodes selection of extreme learning machine for regression. Neurocomputing 73(16):3191–3199. 10th Brazilian symposium on neural networks (SBRN2008) – reference: Montana DJ, Davis L (1989) Training feedforward neural networks using genetic algorithms. In: Proceedings of the 11th international joint conference on artificial intelligence-volume 1, IJCAI’89. Morgan Kaufmann Publishers Inc., San Francisco, pp 762–767 – reference: Sietsma J, Dow RJF (1988) Neural net pruning-why and how. In: IEEE 1988 international conference on neural networks, vol 1, pp 325–333 – reference: Narasimha PL, Delashmit WH, Manry MT, Li J, Maldonado F (2008) An integrated growing-pruning method for feedforward network training. Neurocomputing 71(13), 2831 – 2847. Artificial neural networks (ICANN 2006)/engineering of intelligent systems (ICEIS 2006) – reference: Sabo D, Yu XH (2008) A new pruning algorithm for neural network dimension analysis. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), pp 3313–3318 – reference: HanZWeiBZhengYYinYLiKLiSBreast cancer multi-classification from histopathological images with structured deep learning modelSci Rep2017714172 – reference: Lichman M (2013) UCI machine learning repository – reference: MicheYSorjamaaABasPSimulaOJuttenCLendasseAOp-elm: optimally pruned extreme learning machineIEEE Trans Neural Netw2010211158162 – reference: SetionoRA penalty-function approach for pruning feedforward neural networksNeural Comput1997911852040872.68154 – reference: IslamMMYaoXMuraseKA constructive algorithm for training cooperative neural network ensemblesIEEE Trans Neural Netw2003144820834 – reference: HanHQiaoJA self-organizing fuzzy neural network based on a growing-and-pruning algorithmIEEE Trans Fuzzy Syst201018611291143 – reference: AugastaMGKathirvalavakumarTPruning algorithms of neural networks—a comparative studyCent Eur J Comput Sci201333105115 – reference: FarisHAljarahIMirjaliliSTraining feedforward neural networks using multi-verse optimizer for binary classification problemsAppl Intell2016452322332 – reference: Qiao J, Zhang Y, Han H (2008) Fast unit pruning algorithm for feedforward neural network design. Appl Math Comput 205(2):622 – 627. Special issue on advanced intelligent computing theory and methodology in applied mathematics and computation – reference: MahmudMKaiserMHussainAVassanelliSApplications of deep learning and reinforcement learning to biological dataIEEE Trans Neural Netw Learn Syst201829206320793811399 – reference: KooiTLitjensGvan GinnekenBGubern-MéridaASánchezCIMannRden HeetenAKarssemeijerNLarge scale deep learning for computer aided detection of mammographic lesionsMed Image Anal201735303312 – reference: FnaiechNFnaiechFJervisBCherietMThe combined statistical stepwise and iterative neural network pruning algorithmIntell Autom Soft Comput2009154573589 – reference: Zeraatkar E, Soltani M, Karimaghaee P (2011) A fast convergence algorithm for bpnn based on optimal control theory based learning rate. In: The 2nd international conference on control, instrumentation and automation, pp 292–297 – reference: HagiwaraMA simple and effective method for removal of hidden units and weights. Backpropagation, part IVNeurocomputing199462207218 – reference: ZemouriRZerhouniNAutonomous and adaptive procedure for cumulative failure predictionNeural Comput Appl2012212319331 – reference: Hassibi B, Stork DG, Wolff GJ (1993) Optimal brain surgeon and general network pruning. In: IEEE international conference on neural networks, vol 1, pp 293–299 – reference: DhungelNCarneiroGBradleyAPA deep learning approach for the analysis of masses in mammograms with minimal user interventionMed Image Anal201737114128 – reference: Sun W, Tseng TLB, Zhang J, Qian W (2017) Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Graph 57, 4–9. Recent developments in machine learning for medical imaging applications – reference: Zeiler MD (2012) Adadelta: an adaptive learning rate method. abs/1212.5701 – reference: Slowik A, Bialko M (2008) Training of artificial neural networks using differential evolution algorithm. In: 2008 conference on human system interactions, pp 60–65 – reference: YuHYangXZhengSSunCActive learning from imbalanced data: a solution of online weighted extreme learning machineIEEE Trans Neural Netw Learn Syst201899116 – reference: IslamMMSattarMAAminMFYaoXMuraseKA new constructive algorithm for architectural and functional adaptation of artificial neural networksIEEE Trans Syst Man Cybern Part B (Cybern)200939615901605 – reference: LeCunYBengioYHintonGDeep learningNature20155217553436444 – reference: YaoXA review of evolutionary artificial neural networksInt J Intell Syst199384539567 – reference: SahaMChakrabortyCRacoceanuDEfficient deep learning model for mitosis detection using breast histopathology imagesComput Med Imaging Graph2018642940 – reference: FrancoLJerezJMConstructive neural networks2009Berlin, HeidelbergSpringer – reference: HeHGarciaEALearning from imbalanced dataIEEE Trans Knowl Data Eng200921912631284 – reference: TomèDMontiFBaroffioLBondiLTagliasacchiMTubaroSDeep convolutional neural networks for pedestrian detectionSignal Process Image Commun201647482489 – reference: Fnaiech N, Abid S, Fnaiech F, Cheriet M (2004) A modified version of a formal pruning algorithm based on local relative variance analysis. In: First international symposium on control, communications and signal processing 2004, pp 849–852 – reference: ChenCLPLiuZBroad learning system: an effective and efficient incremental learning system without the need for deep architectureIEEE Trans Neural Netw Learn Syst201829110243746789 – reference: SridharSSPonnavaikkoMA novel constructive neural network architecture based on improved adaptive learning strategy for pattern classification2012BerlinSpringer423433 – reference: HanHGZhangSQiaoJFAn adaptive growing and pruning algorithm for designing recurrent neural networkNeurocomputing20172425162 – reference: AugastaMGKathirvalavakumarTA novel pruning algorithm for optimizing feedforward neural network of classification problemsNeural Process Lett2011343241 – reference: EngelbrechtAPA new pruning heuristic based on variance analysis of sensitivity informationIEEE Trans Neural Netw200112613861399 – reference: LauretPFockEMaraTAA node pruning algorithm based on a Fourier amplitude sensitivity test methodIEEE Trans Neural Netw2006172273293 – reference: SchmidhuberJDeep learning in neural networks: an overviewNeural Netw20156185117 – reference: YangXSDebSCuckoo search: recent advances and applicationsNeural Comput Appl2014241169174 – reference: HuangSCHuangYFBounds on the number of hidden neurons in multilayer perceptronsIEEE Trans Neural Netw1991214755 – reference: ChandraBSharmaRKDeep learning with adaptive learning rate using Laplacian scoreExpert Syst Appl20166317 – reference: Hosseini-AslEZuradaJMNasraouiODeep learning of part-based representation of data using sparse autoencoders with nonnegativity constraintsIEEE Trans Neural Netw Learn Syst2016271224862498 – reference: HuangGBSaratchandranPSundararajanNAn efficient sequential learning algorithm for growing and pruning rbf (gap-rbf) networksIEEE Trans Syst Man Cybern Part B (Cybern)200434622842292 – reference: ZhangRLanYHuangGBXuZBUniversal approximation of extreme learning machine with adaptive growth of hidden nodesIEEE Trans Neural Netw Learn Syst2012232365371 – reference: XuJHoDWA new training and pruning algorithm based on node dependence and Jacobian rank deficiencyNeurocomputing2006701544558 – reference: SahaMChakrabortyCArunIAhmedRChatterjeeSAn advanced deep learning approach for ki-67 stained hotspot detection and proliferation rate scoring for prognostic evaluation of breast cancerSci Rep2017713213 – reference: NayakJNaikBBeheraHA novel nature inspired firefly algorithm with higher order neural network: performance analysisInt J Eng Sci Technol2016191197211 – reference: WanWMabuSShimadaKHirasawaKHuJEnhancing the generalization ability of neural networks through controlling the hidden layersAppl Soft Comput200991404414 – reference: QiaoJLiFHanHLiWConstructive algorithm for fully connected cascade feedforward neural networksNeurocomputing2016182154164 – reference: JiaFLeiYLinJZhouXLuNDeep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive dataMech Syst Signal Process201672–73303315 – reference: Ai F (2011) A new pruning algorithm for feedforward neural networks. In: The fourth international workshop on advanced computational intelligence, pp 286–289 – reference: LeCunYDenkerJSSollaSATouretzkyDSOptimal brain damageAdvances in neural information processing systems 21990BurlingtonMorgan-Kaufmann598605 – reference: Rifai S, Vincent P, Muller X, Glorot X, Bengio Y (2011) Contractive auto-en- coders: explicit invariance during feature extraction. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 833–840 – reference: Zemouri R, Omri N, Devalland C, Arnould L, Morello B, Zerhouni N, Fnaiech F (2018) Breast cancer diagnosis based on joint variable selection and constructive deep neural network. In: 4th IEEE middle east conference on biomedical engineering (MECBME 2018) – reference: Puma-Villanueva WJ, dos Santos EP, Zuben FJV (2012) A constructive algorithm to synthesize arbitrarily connected feedforward neural networks. Neurocomputing 75(1), 14–32. Brazilian symposium on neural networks (SBRN 2010) international conference on hybrid artificial intelligence systems (HAIS 2010) – reference: Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Gordon GJ, Dunson DB (eds) Proceedings of the fourteenth international conference on artificial intelligence and statistics (AISTATS-11), vol 15, Journal of machine learning research—workshop and conference proceedings, pp 315–323 – reference: SubiratsJLFrancoLJerezJMC-mantec: a novel constructive neural network algorithm incorporating competition between neuronsNeural Netw201226130140 – reference: WuXRóżyckiPWilamowskiBMA hybrid constructive algorithm for single-layer feedforward networks learningIEEE Trans Neural Netw Learn Syst2015268165916683454963 – reference: DuffnerSGarciaCAn online backpropagation algorithm with validation error-based adaptive learning rate2007BerlinSpringer249258 – reference: Parekh RG, Yang J, Honavar V (1997) Constructive neural network learning algorithms for multi-category real-valued pattern classification. Technical report. ISU-CS- TR97-06 146. Department of Computer Science, Iowa State Univ – reference: ShresthaSBSongQRobust learning in spikepropNeural Netw20178654681428.68254 – reference: CireşanDCGiustiAGambardellaLMSchmidhuberJMitosis detection in breast cancer histology images with deep neural networks2013BerlinSpringer411418 – reference: Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image classification using convolutional neural networks. In: 2016 international joint conference on neural networks (IJCNN), pp 2560–2567 – reference: Zeng X, Yeung DS (2006) Hidden neuron pruning of multilayer perceptrons using a quantified sensitivity measure. Neurocomputing 69(7):825–837 (2006). New issues in neurocomputing: 13th European symposium on artificial neural networks – reference: Pérez-SánchezBFontenla-RomeroOGuijarro-BerdiñasBA review of adaptive online learning for artificial neural networksArtif Intell Rev201649281299 – reference: Jacek C, Zarzycki H (2003) Application of rough sets in the presumptive diagnosis of urinary system diseases. In: Artificial intelligence and security in computing systems, ACS’2002 9th international conference proceedings. Kluwer Academic Publishers, pp 41–51 – reference: PonnapalliPVSHoKCThomsonMA formal selection and pruning algorithm for feedforward artificial neural network optimizationIEEE Trans Neural Netw1999104964968 – reference: IslamMMSattarMAAminMFYaoXMuraseKA new adaptive merging and growing algorithm for designing artificial neural networksIEEE Trans Syst Man Cybern Part B (Cybern)2009393705722 – reference: HanHGQiaoJFA structure optimisation algorithm for feedforward neural network constructionNeurocomputing201399347357 – reference: Choi B, Lee JH, Kim DH (2008) Solving local minima problem with large number of hidden nodes on two-layered feed-forward artificial neural networks. Neurocomputing 71(16), 3640–3643 (2008). Advances in Neural Information Processing (ICONIP 2006)/Brazilian Symposium on Neural Networks (SBRN 2006) – reference: BengioYCourvilleAVincentPRepresentation learning: a review and new perspectivesIEEE Trans Pattern Anal Mach Intell201335817981828 – reference: Mendes R, Cortez P, Rocha M, Neves J (2002) Particle swarms for feedforward neural network training. In: Proceedings of the 2002 international joint conference on neural networks, 2002. IJCNN ’02, vol 2, pp 1895–1899 – reference: XingHJHuBGTwo-phase construction of multilayer perceptrons using information theoryIEEE Trans Neural Netw2009204715721 – reference: Huynh TQ, Setiono R (2005) Effective neural network pruning using cross-validation. In: Proceedings of the 2005 IEEE international joint conference on neural networks, 2005, vol 2, pp 972–977 – reference: ReedRPruning algorithms—a surveyIEEE Trans Neural Netw199345740747 – reference: VincentPLarochelleHLajoieIBengioYManzagolPAStacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterionJ Mach Learn Res2010113371340827561881242.68256 – reference: CastellanoGFanelliAMPelilloMAn iterative pruning algorithm for feedforward neural networksIEEE Trans Neural Netw199783519531 – reference: WangGA perspective on deep imagingIEEE Access2016489148924 – reference: Zemouri R (2017) An evolutionary building algorithm for deep neural networks. In: 2017 12th international workshop on self-organizing maps and learning vector quantization, clustering and data visualization (WSOM), pp 1–7 – reference: van der MaatenLHintonGVisualizing data using t-sneJ Mach Learn Res2008911257926051225.68219 – reference: WolbergWHMangasarianOMultisurface method of pattern separation for medical diagnosis applied to breast cytologyProc Natl Acad Sci USA199087919391960709.92537 – reference: ShresthaSBSongQAdaptive learning rate of spikeprop based on weight convergence analysisNeural Netw2015631851981325.68197 – reference: GreenspanHvan GinnekenBSummersRMGuest editorial deep learning in medical imaging: overview and future promise of an exciting new techniqueIEEE Trans Med Imaging201635511531159 – reference: ZhangRXuZBHuangGBWangDGlobal convergence of online bp training with dynamic learning rateIEEE Trans Neural Netw Learn Syst2012232330341 – reference: Goodfellow IJ, Warde-farley D, Mirza M, Courville A, Bengio Y (2013) Maxout networks. In: In ICML – reference: Fnaiech N, Fnaiech F, Jervis BW (2011) Feedforward neural networks pruning algorithms, industrial electronics handbook, 2nd edn., vol 5, j.d. irwin, chap. 15, pp 15–1 to 15–15 – reference: ParekhRYangJHonavarVConstructive neural-network learning algorithms for pattern classificationIEEE Trans Neural Netw2000112436451 – reference: AlbarqouniSBaurCAchillesFBelagiannisVDemirciSNavabNAggnet: deep learning from crowds for mitosis detection in breast cancer histology imagesIEEE Trans Med Imaging201635513131321 – reference: WahabNKhanALeeYSTwo-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detectionComput Biol Med2017858697 – reference: BeheraLKumarSPatnaikAOn adaptive learning rate that guarantees convergence in feedforward networksIEEE Trans Neural Netw200617511161125 – reference: XuJLuoXWangGGilmoreHMadabhushiAA deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological imagesNeurocomputing2016191214223 – reference: Nielsen AB, Hansen LK (2008) Structure learning by pruning in independent component analysis. Neurocomputing 71(10):2281–2290. Neurocomputing for vision research advances in blind signal processing – ident: 4196_CR93 doi: 10.1109/MECBME.2018.8402426 – volume: 35 start-page: 1798 issue: 8 year: 2013 ident: 4196_CR6 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2013.50 – volume: 4 start-page: 740 issue: 5 year: 1993 ident: 4196_CR62 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.248452 – volume: 35 start-page: 1153 issue: 5 year: 2016 ident: 4196_CR22 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2016.2553401 – volume: 23 start-page: 365 issue: 2 year: 2012 ident: 4196_CR97 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2011.2178124 – ident: 4196_CR99 doi: 10.1109/ICICIP.2010.5564272 – volume: 8 start-page: 539 issue: 4 year: 1993 ident: 4196_CR89 publication-title: Int J Intell Syst doi: 10.1002/int.4550080406 – volume: 21 start-page: 319 issue: 2 year: 2012 ident: 4196_CR94 publication-title: Neural Comput Appl doi: 10.1007/s00521-011-0585-7 – start-page: 598 volume-title: Advances in neural information processing systems 2 year: 1990 ident: 4196_CR45 – volume: 29 start-page: 2063 year: 2018 ident: 4196_CR48 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2018.2790388 – volume: 9 start-page: 404 issue: 1 year: 2009 ident: 4196_CR81 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2008.01.013 – ident: 4196_CR61 doi: 10.1016/j.amc.2008.05.049 – ident: 4196_CR92 doi: 10.1109/WSOM.2017.8020002 – ident: 4196_CR16 doi: 10.1109/ISCCSP.2004.1296579 – volume: 8 start-page: 630 issue: 3 year: 1997 ident: 4196_CR40 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.572102 – volume: 34 start-page: 241 issue: 3 year: 2011 ident: 4196_CR3 publication-title: Neural Process Lett doi: 10.1007/s11063-011-9196-7 – ident: 4196_CR95 doi: 10.1016/j.neucom.2005.04.010 – ident: 4196_CR21 – volume: 85 start-page: 86 year: 2017 ident: 4196_CR80 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2017.04.012 – volume: 63 start-page: 1 year: 2016 ident: 4196_CR8 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2016.05.022 – volume: 18 start-page: 1129 issue: 6 year: 2010 ident: 4196_CR24 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2010.2070841 – volume: 19 start-page: 197 issue: 1 year: 2016 ident: 4196_CR53 publication-title: Int J Eng Sci Technol – volume: 9 start-page: 185 issue: 1 year: 1997 ident: 4196_CR68 publication-title: Neural Comput doi: 10.1162/neco.1997.9.1.185 – volume: 17 start-page: 1116 issue: 5 year: 2006 ident: 4196_CR5 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2006.878121 – volume: 26 start-page: 1659 issue: 8 year: 2015 ident: 4196_CR84 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2014.2350957 – volume: 49 start-page: 281 year: 2016 ident: 4196_CR57 publication-title: Artif Intell Rev doi: 10.1007/s10462-016-9526-2 – ident: 4196_CR20 – ident: 4196_CR64 doi: 10.1109/IJCNN.2008.4634268 – volume: 63 start-page: 185 year: 2015 ident: 4196_CR69 publication-title: Neural Netw doi: 10.1016/j.neunet.2014.12.001 – volume: 24 start-page: 169 issue: 1 year: 2014 ident: 4196_CR88 publication-title: Neural Comput Appl doi: 10.1007/s00521-013-1367-1 – volume: 20 start-page: 715 issue: 4 year: 2009 ident: 4196_CR85 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2008.2005604 – ident: 4196_CR91 – ident: 4196_CR37 – ident: 4196_CR76 doi: 10.1016/j.compmedimag.2016.07.004 – ident: 4196_CR18 – volume: 6 start-page: 207 issue: 2 year: 1994 ident: 4196_CR23 publication-title: Neurocomputing doi: 10.1016/0925-2312(94)90055-8 – volume: 12 start-page: 1386 issue: 6 year: 2001 ident: 4196_CR14 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.963775 – volume: 61 start-page: 85 year: 2015 ident: 4196_CR67 publication-title: Neural Netw doi: 10.1016/j.neunet.2014.09.003 – volume: 35 start-page: 303 year: 2017 ident: 4196_CR39 publication-title: Med Image Anal doi: 10.1016/j.media.2016.07.007 – volume: 45 start-page: 322 issue: 2 year: 2016 ident: 4196_CR15 publication-title: Appl Intell doi: 10.1007/s10489-016-0767-1 – ident: 4196_CR63 – volume: 242 start-page: 51 year: 2017 ident: 4196_CR26 publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.02.038 – volume-title: Constructive neural networks year: 2009 ident: 4196_CR19 – ident: 4196_CR46 – volume: 99 start-page: 347 year: 2013 ident: 4196_CR25 publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.07.023 – volume: 26 start-page: 130 year: 2012 ident: 4196_CR75 publication-title: Neural Netw doi: 10.1016/j.neunet.2011.10.003 – ident: 4196_CR28 doi: 10.1109/ICNN.1993.298572 – ident: 4196_CR72 doi: 10.1109/HSI.2008.4581409 – volume: 39 start-page: 1590 issue: 6 year: 2009 ident: 4196_CR35 publication-title: IEEE Trans Syst Man Cybern Part B (Cybern) doi: 10.1109/TSMCB.2009.2021849 – volume: 10 start-page: 964 issue: 4 year: 1999 ident: 4196_CR58 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.774273 – volume: 70 start-page: 544 issue: 1 year: 2006 ident: 4196_CR86 publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.11.005 – volume: 86 start-page: 54 year: 2017 ident: 4196_CR70 publication-title: Neural Netw doi: 10.1016/j.neunet.2016.10.011 – volume: 191 start-page: 214 year: 2016 ident: 4196_CR87 publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.01.034 – ident: 4196_CR10 doi: 10.1016/j.neucom.2008.04.004 – ident: 4196_CR1 doi: 10.1109/IWACI.2011.6160018 – volume: 64 start-page: 29 year: 2018 ident: 4196_CR66 publication-title: Comput Med Imaging Graph doi: 10.1016/j.compmedimag.2017.12.001 – ident: 4196_CR59 doi: 10.1016/j.neucom.2011.05.025 – volume: 14 start-page: 820 issue: 4 year: 2003 ident: 4196_CR36 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2003.813832 – ident: 4196_CR77 doi: 10.1109/IJCNN.2003.1223407 – volume: 11 start-page: 3371 year: 2010 ident: 4196_CR79 publication-title: J Mach Learn Res – volume: 21 start-page: 158 issue: 1 year: 2010 ident: 4196_CR50 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2009.2036259 – start-page: 423 volume-title: A novel constructive neural network architecture based on improved adaptive learning strategy for pattern classification year: 2012 ident: 4196_CR74 – ident: 4196_CR51 – ident: 4196_CR96 doi: 10.1109/ICCIAutom.2011.6356672 – volume: 27 start-page: 2486 issue: 12 year: 2016 ident: 4196_CR30 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2015.2479223 – volume: 8 start-page: 519 issue: 3 year: 1997 ident: 4196_CR7 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.572092 – ident: 4196_CR73 doi: 10.1109/IJCNN.2016.7727519 – volume: 87 start-page: 9193 year: 1990 ident: 4196_CR83 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.87.23.9193 – volume: 29 start-page: 10 issue: 1 year: 2018 ident: 4196_CR9 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2017.2716952 – volume: 4 start-page: 8914 year: 2016 ident: 4196_CR82 publication-title: IEEE Access doi: 10.1109/ACCESS.2016.2624938 – volume: 23 start-page: 330 issue: 2 year: 2012 ident: 4196_CR98 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2011.2178315 – volume: 99 start-page: 1 year: 2018 ident: 4196_CR90 publication-title: IEEE Trans Neural Netw Learn Syst – ident: 4196_CR56 – volume: 37 start-page: 114 year: 2017 ident: 4196_CR12 publication-title: Med Image Anal doi: 10.1016/j.media.2017.01.009 – volume: 21 start-page: 1263 issue: 9 year: 2009 ident: 4196_CR29 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2008.239 – volume: 35 start-page: 1313 issue: 5 year: 2016 ident: 4196_CR2 publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2016.2528120 – volume: 34 start-page: 2284 issue: 6 year: 2004 ident: 4196_CR31 publication-title: IEEE Trans Syst Man Cybern Part B (Cybern) doi: 10.1109/TSMCB.2004.834428 – ident: 4196_CR49 doi: 10.1109/IJCNN.2002.1007808 – volume: 182 start-page: 154 year: 2016 ident: 4196_CR60 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.12.003 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 4196_CR44 publication-title: Nature doi: 10.1038/nature14539 – volume: 2 start-page: 47 issue: 1 year: 1991 ident: 4196_CR32 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.80290 – volume: 8 start-page: 1131 issue: 5 year: 1997 ident: 4196_CR41 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.623214 – ident: 4196_CR54 doi: 10.1016/j.neucom.2007.09.016 – volume: 72–73 start-page: 303 year: 2016 ident: 4196_CR38 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2015.10.025 – start-page: 249 volume-title: An online backpropagation algorithm with validation error-based adaptive learning rate year: 2007 ident: 4196_CR13 – volume: 7 start-page: 3213 issue: 1 year: 2017 ident: 4196_CR65 publication-title: Sci Rep doi: 10.1038/s41598-017-03405-5 – start-page: 411 volume-title: Mitosis detection in breast cancer histology images with deep neural networks year: 2013 ident: 4196_CR11 – volume: 17 start-page: 273 issue: 2 year: 2006 ident: 4196_CR43 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2006.871707 – volume: 11 start-page: 436 issue: 2 year: 2000 ident: 4196_CR55 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.839013 – ident: 4196_CR52 doi: 10.1016/j.neucom.2007.08.026 – volume: 15 start-page: 573 issue: 4 year: 2009 ident: 4196_CR17 publication-title: Intell Autom Soft Comput – volume: 9 start-page: 2579 issue: 11 year: 2008 ident: 4196_CR47 publication-title: J Mach Learn Res – volume: 39 start-page: 705 issue: 3 year: 2009 ident: 4196_CR34 publication-title: IEEE Trans Syst Man Cybern Part B (Cybern) doi: 10.1109/TSMCB.2008.2008724 – ident: 4196_CR71 doi: 10.1109/ICNN.1988.23864 – ident: 4196_CR42 doi: 10.1016/j.neucom.2010.05.022 – ident: 4196_CR33 – volume: 3 start-page: 105 issue: 3 year: 2013 ident: 4196_CR4 publication-title: Cent Eur J Comput Sci – volume: 7 start-page: 4172 issue: 1 year: 2017 ident: 4196_CR27 publication-title: Sci Rep doi: 10.1038/s41598-017-04075-z – volume: 47 start-page: 482 year: 2016 ident: 4196_CR78 publication-title: Signal Process Image Commun doi: 10.1016/j.image.2016.05.007 |
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