A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited–memory BFGS optimization algorithms
Working up with deep learning techniques requires profound understanding of the mechanisms underlying the optimization of the internal parameters of complex structures. The major factor limiting this understanding is that there exist only a few optimization methods such as gradient descent and Limit...
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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 266; s. 506 - 526 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
29.11.2017
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| Témata: | |
| ISSN: | 0925-2312, 1872-8286 |
| On-line přístup: | Získat plný text |
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| Abstract | Working up with deep learning techniques requires profound understanding of the mechanisms underlying the optimization of the internal parameters of complex structures. The major factor limiting this understanding is that there exist only a few optimization methods such as gradient descent and Limited–memory Broyden–Fletcher–Goldfarb–Shannon (L-BFGS) to find the best local minima of the problem space for these complex structures such as deep neural network (DNN). Therefore, in this paper, we represent a new training approach named hybrid artificial bee colony based training strategy (HABCbTS) to tune the parameters of a DNN structure, which includes one or more autoencoder layers cascaded to a softmax classification layer. In this strategy, a derivative-free optimization algorithm “ABC” is combined with a derivative-based algorithm “L-BFGS” to construct “HABC”, which is used in the HABCbTS. Detailed simulation results supported by statistical analysis show that the proposed training strategy results in better classification performance compared to the DNN classifier trained with the L-BFGS, ABC and modified ABC. The obtained classification results are also compared with the state-of-the-art classifiers, including MLP, SVM, KNN, DT and NB on 15 data sets with different dimensions and sizes. |
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| AbstractList | Working up with deep learning techniques requires profound understanding of the mechanisms underlying the optimization of the internal parameters of complex structures. The major factor limiting this understanding is that there exist only a few optimization methods such as gradient descent and Limited–memory Broyden–Fletcher–Goldfarb–Shannon (L-BFGS) to find the best local minima of the problem space for these complex structures such as deep neural network (DNN). Therefore, in this paper, we represent a new training approach named hybrid artificial bee colony based training strategy (HABCbTS) to tune the parameters of a DNN structure, which includes one or more autoencoder layers cascaded to a softmax classification layer. In this strategy, a derivative-free optimization algorithm “ABC” is combined with a derivative-based algorithm “L-BFGS” to construct “HABC”, which is used in the HABCbTS. Detailed simulation results supported by statistical analysis show that the proposed training strategy results in better classification performance compared to the DNN classifier trained with the L-BFGS, ABC and modified ABC. The obtained classification results are also compared with the state-of-the-art classifiers, including MLP, SVM, KNN, DT and NB on 15 data sets with different dimensions and sizes. |
| Author | Badem, Hasan Yuksel, Mehmet Emin Caliskan, Abdullah Basturk, Alper |
| Author_xml | – sequence: 1 givenname: Hasan surname: Badem fullname: Badem, Hasan email: hbadem@erciyes.edu.tr organization: Department of Computer Engineering, Erciyes University, Kayseri, Turkey – sequence: 2 givenname: Alper orcidid: 0000-0001-5810-0643 surname: Basturk fullname: Basturk, Alper organization: Department of Computer Engineering, Erciyes University, Kayseri, Turkey – sequence: 3 givenname: Abdullah surname: Caliskan fullname: Caliskan, Abdullah organization: Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey – sequence: 4 givenname: Mehmet Emin surname: Yuksel fullname: Yuksel, Mehmet Emin organization: Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey |
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| Cites_doi | 10.1162/08997660260293319 10.1016/j.neucom.2015.09.116 10.1504/IJDMB.2016.075823 10.1007/BF01589116 10.14311/NNW.2011.21.028 10.1090/S0025-5718-1980-0572855-7 10.1126/science.1127647 10.1016/0004-3702(89)90049-0 10.1007/978-3-642-35289-8_26 10.1016/j.neucom.2017.01.090 10.1109/TCBB.2011.140 10.3389/fnhum.2011.00076 10.1016/j.engappai.2016.01.032 10.5755/j01.eie.23.2.18002 10.1016/j.ins.2010.07.015 10.1016/j.asoc.2007.05.007 10.1016/j.neucom.2016.12.038 10.1016/j.neucom.2016.09.018 10.1016/j.neucom.2016.09.010 10.1016/j.neucom.2015.10.064 10.1016/j.asoc.2016.05.021 10.1007/s10957-012-0107-5 10.1007/s12559-016-9404-x 10.1016/j.asoc.2010.12.001 10.1007/s10898-007-9149-x 10.1007/s12559-016-9396-6 10.1038/nature14539 10.1109/TNN.2002.804317 10.1016/j.ins.2013.08.035 |
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| Keywords | Deep learning Deep neural network Stacked autoencoder network Artificial bee colony optimization algorithm L-BFGS Hybridization Training strategy |
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| References | Karaboga (bib0032) 2005 Chen, Liu, Zhang, Liang, Suganthan, Qu (bib0050) 2014 Leung, Lam, Ling, Tam (bib0023) 2003; 14 Karaboga, Aslan (bib0039) 2016; 16 Karaboga, Basturk (bib0033) 2007; 39 Basturk, Akay (bib0036) 2012; 155 Hinton (bib0001) 1989; 40 A. Ng, “Sparse autoencoder,” CS294A Lecture Notes, Stanford Univ., Stanford, CA, USA, Tech. Rep., 2011. Vincent, Larochelle, Bengio, Manzagol (bib0015) 2008 Henson, Wakeman, Litvak, Friston (bib0044) 2011; 5 Recognition of Convex Sets. Y. Bengio, Practical Recommendations for Gradient-Based Training of Deep Architectures, Springer Berlin Heidelberg, pp. 437–478. Zhang, Zhang, Chen (bib0028) 2016; 50 Ranzato, Poultney, Chopra, LeCun (bib0025) 2006 Berahas, Nocedal, Takác (bib0031) 2016 Guo, Liu, Oerlemans, Lao, Wu, Lew (bib0013) 2016; 187 Olivetti, Kia, Avesani (bib0045) 2014 Huang, Siniscalchi, Lee (bib0006) 2016; 218 Ngiam, Coates, Lahiri, Prochnow, Le, Ng (bib0019) 2011 Yu, Jia, Xu (bib0008) 2017; 219 Badem, Caliskan, Basturk, Yuksel (bib0010) 2016 Zeng, Wang, Zhang, Alsaadi (bib0017) 2016; 8 DecMeg2014-Decoding the Human Brain. Basturk, Akay (bib0020) 2013; 253 LeCun, Bengio, Hinton (bib0012) 2015; 521 Nocedal (bib0029) 1980; 35 Akay, Karaboga (bib0040) 2012; 192 . Akay, Karaboga (bib0035) 2011; 11 Caliskan, Yuksel, Badem, Basturk (bib0047) 2017; 23 Karaboga, Basturk (bib0034) 2008; 8 Bengio, Lamblin, Popovici, Larochelle (bib0004) 2007 Karaboga, Ozturk (bib0022) 2009; 19 Utgoff, Stracuzzi (bib0002) 2002; 14 Zeng, Wang, Li, Du, Liu (bib0016) 2012; 9 Liu, Wang, Liu, Zeng, Liu, Alsaadi (bib0014) 2017; 234 Ozkan, Ozturk, Sunar, Karaboga (bib0021) 2011; 21 Ozturk, Aslan (bib0037) 2016; 14 Hinton, Salakhutdinov (bib0003) 2006; 313 Rectangles and Rectangles-Images Data. Bengio, LeCun (bib0005) 2007; 34 Basturk, Yuksel, Caliskan, Badem (bib0043) 2017 Badem, Caliskan, Basturk, Yuksel (bib0009) 2016 Wu, Lu, Gao, Zhao, Liu (bib0007) 2016; 175 Liu, Zhang, Wu, Liang, Wang, Teo (bib0041) 2016; 47 Zeng, Zhang, Liu, Liang, Alsaadi (bib0018) 2017; 240 Liu, Nocedal (bib0030) 1989; 45 M. Lichman, UCI Machine Learning Repository, 2013 Karaboga, Aslan (bib0038) 2015; 15 Karaboga, Akay (bib0024) 2009; 214 Zeng, Wang, Zhang, Liu, Alsaadi (bib0011) 2016; 8 Karaboga (10.1016/j.neucom.2017.05.061_bib0038) 2015; 15 Basturk (10.1016/j.neucom.2017.05.061_bib0043) 2017 Bengio (10.1016/j.neucom.2017.05.061_bib0005) 2007; 34 Badem (10.1016/j.neucom.2017.05.061_bib0009) 2016 Guo (10.1016/j.neucom.2017.05.061_bib0013) 2016; 187 10.1016/j.neucom.2017.05.061_bib0048 Berahas (10.1016/j.neucom.2017.05.061_bib0031) 2016 Karaboga (10.1016/j.neucom.2017.05.061_bib0022) 2009; 19 10.1016/j.neucom.2017.05.061_bib0049 Akay (10.1016/j.neucom.2017.05.061_bib0040) 2012; 192 Liu (10.1016/j.neucom.2017.05.061_bib0014) 2017; 234 Liu (10.1016/j.neucom.2017.05.061_bib0041) 2016; 47 Zhang (10.1016/j.neucom.2017.05.061_bib0028) 2016; 50 Bengio (10.1016/j.neucom.2017.05.061_bib0004) 2007 Hinton (10.1016/j.neucom.2017.05.061_bib0003) 2006; 313 Zeng (10.1016/j.neucom.2017.05.061_bib0011) 2016; 8 10.1016/j.neucom.2017.05.061_bib0042 Olivetti (10.1016/j.neucom.2017.05.061_bib0045) 2014 10.1016/j.neucom.2017.05.061_bib0046 Akay (10.1016/j.neucom.2017.05.061_bib0035) 2011; 11 Karaboga (10.1016/j.neucom.2017.05.061_bib0033) 2007; 39 Caliskan (10.1016/j.neucom.2017.05.061_bib0047) 2017; 23 Zeng (10.1016/j.neucom.2017.05.061_bib0017) 2016; 8 Chen (10.1016/j.neucom.2017.05.061_bib0050) 2014 Yu (10.1016/j.neucom.2017.05.061_bib0008) 2017; 219 Utgoff (10.1016/j.neucom.2017.05.061_bib0002) 2002; 14 Henson (10.1016/j.neucom.2017.05.061_bib0044) 2011; 5 Hinton (10.1016/j.neucom.2017.05.061_bib0001) 1989; 40 Basturk (10.1016/j.neucom.2017.05.061_bib0036) 2012; 155 Ngiam (10.1016/j.neucom.2017.05.061_bib0019) 2011 Karaboga (10.1016/j.neucom.2017.05.061_bib0032) 2005 Badem (10.1016/j.neucom.2017.05.061_bib0010) 2016 Wu (10.1016/j.neucom.2017.05.061_bib0007) 2016; 175 10.1016/j.neucom.2017.05.061_bib0026 Karaboga (10.1016/j.neucom.2017.05.061_bib0039) 2016; 16 Zeng (10.1016/j.neucom.2017.05.061_bib0018) 2017; 240 10.1016/j.neucom.2017.05.061_bib0027 Ozturk (10.1016/j.neucom.2017.05.061_bib0037) 2016; 14 Zeng (10.1016/j.neucom.2017.05.061_bib0016) 2012; 9 Leung (10.1016/j.neucom.2017.05.061_bib0023) 2003; 14 Ranzato (10.1016/j.neucom.2017.05.061_bib0025) 2006 Vincent (10.1016/j.neucom.2017.05.061_bib0015) 2008 LeCun (10.1016/j.neucom.2017.05.061_bib0012) 2015; 521 Nocedal (10.1016/j.neucom.2017.05.061_bib0029) 1980; 35 Basturk (10.1016/j.neucom.2017.05.061_bib0020) 2013; 253 Karaboga (10.1016/j.neucom.2017.05.061_bib0034) 2008; 8 Huang (10.1016/j.neucom.2017.05.061_bib0006) 2016; 218 Liu (10.1016/j.neucom.2017.05.061_bib0030) 1989; 45 Ozkan (10.1016/j.neucom.2017.05.061_bib0021) 2011; 21 Karaboga (10.1016/j.neucom.2017.05.061_bib0024) 2009; 214 |
| References_xml | – start-page: 1137 year: 2006 end-page: 1144 ident: bib0025 article-title: Efficient learning of sparse representations with an energy-based model publication-title: Advances in Neural Information Processing Systems – volume: 14 start-page: 2497 year: 2002 end-page: 2529 ident: bib0002 article-title: Many-Layered Learning publication-title: Neural Comput. – volume: 8 start-page: 684 year: 2016 end-page: 692 ident: bib0011 article-title: Deep belief networks for quantitative analysis of a gold immunochromatographic strip publication-title: Cognitive Comput. – start-page: 265 year: 2011 end-page: 272 ident: bib0019 article-title: On optimization methods for deep learning publication-title: Proceedings of the 28th International Conference on Machine Learning (ICML-11) – volume: 21 start-page: 473 year: 2011 end-page: 492 ident: bib0021 article-title: The artificial bee colony algorithm in training artificial neural network for oil spill detection publication-title: Neural Netw. World – volume: 11 start-page: 3021 year: 2011 end-page: 3031 ident: bib0035 article-title: A modified artificial bee colony (ABC) algorithm for constrained optimization problems publication-title: Appl. Soft Comput. – reference: Rectangles and Rectangles-Images Data. – reference: A. Ng, “Sparse autoencoder,” CS294A Lecture Notes, Stanford Univ., Stanford, CA, USA, Tech. Rep., 2011. – reference: Recognition of Convex Sets. – volume: 14 start-page: 332 year: 2016 end-page: 353 ident: bib0037 article-title: A new artificial bee colony algorithm to solve the multiple sequence alignment problem publication-title: Int. J. Data Mining Bioinform. – volume: 8 start-page: 143 year: 2016 end-page: 152 ident: bib0017 article-title: A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay publication-title: Cognitive Comput. – reference: DecMeg2014-Decoding the Human Brain. – volume: 214 start-page: 108 year: 2009 end-page: 132 ident: bib0024 article-title: A comparative study of artificial bee colony algorithm publication-title: Appl. Math. Comput. – start-page: 153 year: 2007 end-page: 160 ident: bib0004 article-title: Greedy Layer-Wise Training of Deep Networks publication-title: Proceedings of 19th the Advances in Neural Information Processing Systems – volume: 219 start-page: 88 year: 2017 end-page: 98 ident: bib0008 article-title: Convolutional neural networks for hyperspectral image classification publication-title: Neurocomputing – reference: Y. Bengio, Practical Recommendations for Gradient-Based Training of Deep Architectures, Springer Berlin Heidelberg, pp. 437–478. – volume: 8 start-page: 687 year: 2008 end-page: 697 ident: bib0034 article-title: On the performance of artificial bee colony (ABC) algorithm publication-title: Appl. Soft Comput. – volume: 45 start-page: 503 year: 1989 end-page: 528 ident: bib0030 article-title: On the limited memory BFGS method for large scale optimization publication-title: Math. Progr. – volume: 155 start-page: 1095 year: 2012 end-page: 1104 ident: bib0036 article-title: Parallel implementation of synchronous type artificial bee colony algorithm for global optimization publication-title: J. Optim. Theory Appl. – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: bib0012 article-title: Deep learning publication-title: Nature – volume: 39 start-page: 459 year: 2007 end-page: 471 ident: bib0033 article-title: A powerful and efficient algorithm for numerical function optimization: artificial Bee colony (ABC) algorithm publication-title: Global Optim. – year: 2005 ident: bib0032 article-title: An idea based on honey bee swarm for numerical optimization publication-title: Technical Report-TR06 – volume: 34 start-page: 1 year: 2007 end-page: 41 ident: bib0005 article-title: Scaling learning algorithms towards AI publication-title: Large-Scale Kernel Mach. – volume: 35 start-page: 773 year: 1980 end-page: 782 ident: bib0029 article-title: Updating Quasi-Newton Matrices with Limited Storage publication-title: Math. Comput. – reference: M. Lichman, UCI Machine Learning Repository, 2013, – start-page: 370 year: 2016 end-page: 373 ident: bib0010 article-title: Classification of human activity by using a stacked autoencoder publication-title: Proceedings of Medical Technologies National Conference (TIPTEKNO) – volume: 5 start-page: 1 year: 2011 end-page: 16 ident: bib0044 article-title: A parametric empirical bayesian framework for the eeg/meg inverse problem: generative models for multi-subject and multi-modal integration publication-title: Frontiers Human Neurosci. – start-page: 1 year: 2014 end-page: 4 ident: bib0045 article-title: meg decoding across subjects publication-title: Proceedings of the International Workshop on Pattern Recognition in Neuroimaging – volume: 19 start-page: 279 year: 2009 end-page: 292 ident: bib0022 article-title: Neural networks training by artificial bee colony algorithm on pattern classification publication-title: Neural Netw. World – volume: 14 start-page: 79 year: 2003 end-page: 88 ident: bib0023 article-title: Tuning of the structure and parameters of a neural network using an improved genetic algorithm publication-title: IEEE Trans. Neural Netw. – year: 2017 ident: bib0043 article-title: Deep Neural Network Classifier for Hand Movement Prediction publication-title: Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference SIU – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: bib0003 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – start-page: 1055 year: 2016 end-page: 1063 ident: bib0031 article-title: A multi-batch L-BFGS method for machine learning publication-title: Advances in Neural Information Processing Systems – volume: 47 start-page: 281 year: 2016 end-page: 294 ident: bib0041 article-title: A hybrid approach to constrained global optimization publication-title: Appl. Soft Comput. – year: 2014 ident: bib0050 article-title: Problem definitions and evaluation criteria for CEC 2015 special session on bound constrained single-objective computationally expensive numerical optimization publication-title: Technical Report – start-page: 1096 year: 2008 end-page: 1103 ident: bib0015 article-title: Extracting and composing robust features with denoising autoencoders publication-title: Proceedings of the 25th International Conference on Machine Learning – volume: 187 start-page: 27 year: 2016 end-page: 48 ident: bib0013 article-title: Deep learning for visual understanding: a review publication-title: Neurocomputing – volume: 50 start-page: 245 year: 2016 end-page: 255 ident: bib0028 article-title: Deep neural network for halftone image classification based on sparse auto-encoder publication-title: Eng. Appl. Artif. Intel. – volume: 15 start-page: 1 year: 2015 end-page: 11 ident: bib0038 article-title: A discrete artificial bee colony algorithm for detecting transcription factor binding sites in dna sequences publication-title: Genetics Molecular Res. GMR – volume: 16 start-page: 2055 year: 2016 end-page: 2064 ident: bib0039 article-title: Best supported emigrant creation for parallel implementation of artificial Bee colony algorithm publication-title: IU-J. Electr. Electron. Eng. – volume: 253 start-page: 34 year: 2013 end-page: 55 ident: bib0020 article-title: Performance analysis of the coarse-Grained Parallel model of the artificial bee colony algorithm publication-title: Inform. Sci. – volume: 218 start-page: 448 year: 2016 end-page: 459 ident: bib0006 article-title: A unified approach to transfer learning of deep neural networks with applications to speaker adaptation in automatic speech recognition publication-title: Neurocomputing – volume: 23 start-page: 63 year: 2017 end-page: 67 ident: bib0047 article-title: A deep neural network classifier for decoding human brain activity based on magnetoencephalography publication-title: Elektronika ir Elektrotechnika – volume: 192 start-page: 120 year: 2012 end-page: 142 ident: bib0040 article-title: A modified artificial bee colony algorithm for real-parameter optimization publication-title: Inform. Sci. – volume: 9 start-page: 321 year: 2012 end-page: 329 ident: bib0016 article-title: A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. – volume: 240 start-page: 175 year: 2017 end-page: 182 ident: bib0018 article-title: A switching delayed PSO optimized extreme learning machine for short-term load forecasting publication-title: Neurocomputing – reference: . – start-page: 499 year: 2016 end-page: 502 ident: bib0009 article-title: Classification and diagnosis of the Parkinson disease by stacked autoencoder publication-title: Proceedings of 9th National Conference on Electrical and Electronics Engineering, ELECO – volume: 175 start-page: 310 year: 2016 end-page: 323 ident: bib0007 article-title: Regional deep learning model for visual tracking publication-title: Neurocomputing – volume: 234 start-page: 11 year: 2017 end-page: 26 ident: bib0014 article-title: A survey of deep neural network architectures and their applications publication-title: Neurocomputing – volume: 40 start-page: 185 year: 1989 end-page: 234 ident: bib0001 article-title: Connectionist Learning Procedures publication-title: Artif. Intel. – volume: 14 start-page: 2497 issue: 10 year: 2002 ident: 10.1016/j.neucom.2017.05.061_bib0002 article-title: Many-Layered Learning publication-title: Neural Comput. doi: 10.1162/08997660260293319 – volume: 187 start-page: 27 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0013 article-title: Deep learning for visual understanding: a review publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.09.116 – volume: 14 start-page: 332 issue: 4 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0037 article-title: A new artificial bee colony algorithm to solve the multiple sequence alignment problem publication-title: Int. J. Data Mining Bioinform. doi: 10.1504/IJDMB.2016.075823 – volume: 45 start-page: 503 issue: 1 year: 1989 ident: 10.1016/j.neucom.2017.05.061_bib0030 article-title: On the limited memory BFGS method for large scale optimization publication-title: Math. Progr. doi: 10.1007/BF01589116 – volume: 21 start-page: 473 issue: 6 year: 2011 ident: 10.1016/j.neucom.2017.05.061_bib0021 article-title: The artificial bee colony algorithm in training artificial neural network for oil spill detection publication-title: Neural Netw. World doi: 10.14311/NNW.2011.21.028 – volume: 35 start-page: 773 issue: 151 year: 1980 ident: 10.1016/j.neucom.2017.05.061_bib0029 article-title: Updating Quasi-Newton Matrices with Limited Storage publication-title: Math. Comput. doi: 10.1090/S0025-5718-1980-0572855-7 – volume: 313 start-page: 504 issue: 5786 year: 2006 ident: 10.1016/j.neucom.2017.05.061_bib0003 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – volume: 40 start-page: 185 issue: 1 year: 1989 ident: 10.1016/j.neucom.2017.05.061_bib0001 article-title: Connectionist Learning Procedures publication-title: Artif. Intel. doi: 10.1016/0004-3702(89)90049-0 – ident: 10.1016/j.neucom.2017.05.061_bib0027 doi: 10.1007/978-3-642-35289-8_26 – volume: 240 start-page: 175 year: 2017 ident: 10.1016/j.neucom.2017.05.061_bib0018 article-title: A switching delayed PSO optimized extreme learning machine for short-term load forecasting publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.01.090 – volume: 9 start-page: 321 issue: 2 year: 2012 ident: 10.1016/j.neucom.2017.05.061_bib0016 article-title: A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models publication-title: IEEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2011.140 – volume: 5 start-page: 1 year: 2011 ident: 10.1016/j.neucom.2017.05.061_bib0044 article-title: A parametric empirical bayesian framework for the eeg/meg inverse problem: generative models for multi-subject and multi-modal integration publication-title: Frontiers Human Neurosci. doi: 10.3389/fnhum.2011.00076 – year: 2017 ident: 10.1016/j.neucom.2017.05.061_bib0043 article-title: Deep Neural Network Classifier for Hand Movement Prediction – volume: 19 start-page: 279 issue: 3 year: 2009 ident: 10.1016/j.neucom.2017.05.061_bib0022 article-title: Neural networks training by artificial bee colony algorithm on pattern classification publication-title: Neural Netw. World – start-page: 1137 year: 2006 ident: 10.1016/j.neucom.2017.05.061_bib0025 article-title: Efficient learning of sparse representations with an energy-based model – ident: 10.1016/j.neucom.2017.05.061_bib0026 – ident: 10.1016/j.neucom.2017.05.061_bib0049 – volume: 50 start-page: 245 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0028 article-title: Deep neural network for halftone image classification based on sparse auto-encoder publication-title: Eng. Appl. Artif. Intel. doi: 10.1016/j.engappai.2016.01.032 – volume: 15 start-page: 1 issue: 2 year: 2015 ident: 10.1016/j.neucom.2017.05.061_bib0038 article-title: A discrete artificial bee colony algorithm for detecting transcription factor binding sites in dna sequences publication-title: Genetics Molecular Res. GMR – start-page: 499 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0009 article-title: Classification and diagnosis of the Parkinson disease by stacked autoencoder – start-page: 1055 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0031 article-title: A multi-batch L-BFGS method for machine learning – volume: 23 start-page: 63 issue: 2 year: 2017 ident: 10.1016/j.neucom.2017.05.061_bib0047 article-title: A deep neural network classifier for decoding human brain activity based on magnetoencephalography publication-title: Elektronika ir Elektrotechnika doi: 10.5755/j01.eie.23.2.18002 – start-page: 1 year: 2014 ident: 10.1016/j.neucom.2017.05.061_bib0045 article-title: meg decoding across subjects – volume: 192 start-page: 120 year: 2012 ident: 10.1016/j.neucom.2017.05.061_bib0040 article-title: A modified artificial bee colony algorithm for real-parameter optimization publication-title: Inform. Sci. doi: 10.1016/j.ins.2010.07.015 – volume: 8 start-page: 687 issue: 1 year: 2008 ident: 10.1016/j.neucom.2017.05.061_bib0034 article-title: On the performance of artificial bee colony (ABC) algorithm publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2007.05.007 – volume: 234 start-page: 11 year: 2017 ident: 10.1016/j.neucom.2017.05.061_bib0014 article-title: A survey of deep neural network architectures and their applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.12.038 – ident: 10.1016/j.neucom.2017.05.061_bib0048 – volume: 218 start-page: 448 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0006 article-title: A unified approach to transfer learning of deep neural networks with applications to speaker adaptation in automatic speech recognition publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.018 – volume: 219 start-page: 88 year: 2017 ident: 10.1016/j.neucom.2017.05.061_bib0008 article-title: Convolutional neural networks for hyperspectral image classification publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.010 – volume: 175 start-page: 310 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0007 article-title: Regional deep learning model for visual tracking publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.10.064 – volume: 47 start-page: 281 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0041 article-title: A hybrid approach to constrained global optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2016.05.021 – year: 2014 ident: 10.1016/j.neucom.2017.05.061_bib0050 article-title: Problem definitions and evaluation criteria for CEC 2015 special session on bound constrained single-objective computationally expensive numerical optimization – start-page: 370 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0010 article-title: Classification of human activity by using a stacked autoencoder – volume: 155 start-page: 1095 issue: 3 year: 2012 ident: 10.1016/j.neucom.2017.05.061_bib0036 article-title: Parallel implementation of synchronous type artificial bee colony algorithm for global optimization publication-title: J. Optim. Theory Appl. doi: 10.1007/s10957-012-0107-5 – volume: 214 start-page: 108 issue: 1 year: 2009 ident: 10.1016/j.neucom.2017.05.061_bib0024 article-title: A comparative study of artificial bee colony algorithm publication-title: Appl. Math. Comput. – volume: 16 start-page: 2055 issue: 2 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0039 article-title: Best supported emigrant creation for parallel implementation of artificial Bee colony algorithm publication-title: IU-J. Electr. Electron. Eng. – start-page: 1096 year: 2008 ident: 10.1016/j.neucom.2017.05.061_bib0015 article-title: Extracting and composing robust features with denoising autoencoders – ident: 10.1016/j.neucom.2017.05.061_bib0042 – start-page: 153 year: 2007 ident: 10.1016/j.neucom.2017.05.061_bib0004 article-title: Greedy Layer-Wise Training of Deep Networks – volume: 34 start-page: 1 issue: 5 year: 2007 ident: 10.1016/j.neucom.2017.05.061_bib0005 article-title: Scaling learning algorithms towards AI publication-title: Large-Scale Kernel Mach. – volume: 8 start-page: 684 issue: 4 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0011 article-title: Deep belief networks for quantitative analysis of a gold immunochromatographic strip publication-title: Cognitive Comput. doi: 10.1007/s12559-016-9404-x – volume: 11 start-page: 3021 issue: 3 year: 2011 ident: 10.1016/j.neucom.2017.05.061_bib0035 article-title: A modified artificial bee colony (ABC) algorithm for constrained optimization problems publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2010.12.001 – volume: 39 start-page: 459 issue: 3 year: 2007 ident: 10.1016/j.neucom.2017.05.061_bib0033 article-title: A powerful and efficient algorithm for numerical function optimization: artificial Bee colony (ABC) algorithm publication-title: Global Optim. doi: 10.1007/s10898-007-9149-x – volume: 8 start-page: 143 issue: 2 year: 2016 ident: 10.1016/j.neucom.2017.05.061_bib0017 article-title: A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay publication-title: Cognitive Comput. doi: 10.1007/s12559-016-9396-6 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.neucom.2017.05.061_bib0012 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 14 start-page: 79 issue: 1 year: 2003 ident: 10.1016/j.neucom.2017.05.061_bib0023 article-title: Tuning of the structure and parameters of a neural network using an improved genetic algorithm publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2002.804317 – start-page: 265 year: 2011 ident: 10.1016/j.neucom.2017.05.061_bib0019 article-title: On optimization methods for deep learning – ident: 10.1016/j.neucom.2017.05.061_bib0046 – volume: 253 start-page: 34 year: 2013 ident: 10.1016/j.neucom.2017.05.061_bib0020 article-title: Performance analysis of the coarse-Grained Parallel model of the artificial bee colony algorithm publication-title: Inform. Sci. doi: 10.1016/j.ins.2013.08.035 – year: 2005 ident: 10.1016/j.neucom.2017.05.061_bib0032 article-title: An idea based on honey bee swarm for numerical optimization |
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