Search Results - "Automation/classification"

Specific categorical Modelling System, third stage

Authors: García Pedraza, Rubén

Subject Terms: Categorical Critiques, Automation/classification, Automation, Laboratory, inner psychology, Automation/ethics, Home automation, Office Automation/history, Automation, Laboratory/classification, Library Automation/trends, Automation/methods, Automation/economics, Automation/instrumentation, Library Automation, Office Automation/ethics, Automation, artificial psychology, Office Automation/standards, Office Automation, decision automation, Automation/standards, Library Automation/classification, Automation/history, Library Automation/standards

Growing Ai Understanding for Applied Research and Successful Education

Authors: IJRAME Journal

Subject Terms: Automation/classification, Machine Learning/ethics, Artificial intelligence, Artificial Intelligence/legislation & jurisprudence, Artificial Intelligence/statistics & numerical data, Artificial Intelligence/economics, Automation/ethics, Supervised Machine Learning/trends, Unsupervised Machine Learning/history, Artificial Intelligence/standards, Unsupervised Machine Learning/classification, Education, Machine Learning, Automation, Artificial Intelligence/history, Supervised Machine Learning/economics, Artificial Intelligence, Supervised Machine Learning/standards, Personalized learning, Machine learning, Machine Learning/classification, Educational Personnel/education, Artificial Intelligence/trends, Machine Learning/standards, Machine Learning/legislation & jurisprudence, Automation/history, data privacy, Artificial Intelligence/ethics, Research, Supervised Machine Learning/ethics, Machine Learning/supply & distribution, Automation/methods, Supervised Machine Learning/classification, Artificial Intelligence/supply & distribution, Machine Learning/economics, Machine Learning/trends, Unsupervised Machine Learning/ethics, Algorithmic bias, Machine Learning/history, Educational Technology/education, Artificial Intelligence/classification, Automation/standards, Supervised Machine Learning, Unsupervised Machine Learning

PROBLEMS OF THE USE OF DIGITAL TECHNOLOGIES IN ACCOUNTING AND AUDIT

Authors: Rakhimov, G.

Subject Terms: Internet services, information and telecommunication technology (ICT), audit of financial statements, 1C-Accounting, accounting automation, classification of software products, information processing, electronic services

Automated digital image analyses for estimating percent ground cover of winter wheat based on object features

Authors: LI Cun-jun WANG Ji-hua LIU Liang-yun et al.

Source: 浙江大学学报. 农业与生命科学版, Vol 30, Pp 650-656 (2004)

Subject Terms: percent ground cover, image analyses, features extraction, automation classification, Biology (General), QH301-705.5, Agriculture (General), S1-972

File Description: electronic resource

Relation: https://doaj.org/toc/1008-9209; https://doaj.org/toc/2097-5155

Access URL: https://doaj.org/article/040e526b071f4ba1bc9263a0fe218b8b

一種超環面拓樸結構的自組織映射網路結合2-Mean方法為基的自動劃分群集演算法 ; A Wrap-arounded Self-Organizing Map Associated with a 2-Mean Method Based Automation Classification Mechanism

Authors: 宋德進 Sung, Te-Chin 楊烽正 et al.

Contributors: 宋德進 Sung, Te-Chin 楊烽正 et al.

Subject Terms: 物件分群, 自組織映射網路, 超環面拓樸結構的自組織映射網路, 自動劃分群集演算法, automation classification mechanism, Wrap-arounded SOM, Self-Organizing Map, object grouping

File Description: 3028887 bytes; application/pdf

Relation: 參考文獻 英文文獻： Balakrishnan, P. V., Cooper, M. C., Jacob, V. S., and Lewis, P. A. (1994) “A study of the classification capability of neural networks using unsupervised learning: A comparison with k-means clustering,” Psychometrika, Vol. 59, No. 4, pp. 509-525. Carlos, C. S. (1996) “Self-organizing neural networks for financial diagnosis,” Decision support systems, Vol. 17, pp. 227-238. Deboeck, G. and Kohonen, T. (1998) Visual Explorations in Finance: with Self-Organizing Maps. Berlin: Springer. Flexer, A. (2001) “On the use of self-organizing maps for clustering and visualization,’ Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Didcovery, Vol. 1704, pp. 80-88. Hines, J. W. (1997) MATLAB Supplement to Fuzzy and Neural Approaches in Engineering. John Wiley and Sons Inc. Kiang, M. Y., Kulkarni, U. R., and Tam, K. Y. (1995) “Self-organizing map network as an interactive clustering tool – An application to group technology,” Decision support systems, Vol. 15, pp. 351-374. Kohonen, T (1995) Self-Organizing Map. Berlin: Springer. Nour, M. A. and Madey, G. R. (1996) “Heuristic and optimization approaches to extending the kohonen self organizing algorithm,” European Journal of Operational Research, Vol. 93, pp. 428-448. Mangiameli, P., Chen, S. K., and West, D. (1996) “A comparison of SOM neural network and hierarchical clustering methods,” European Journal of Operational Research, Vol. 93, pp. 402-417. Merkl, D. and Rauber, A. (1997) “Alternative ways for cluster visualization in self-organizing maps,” Proceedings of the Workshop on Self-organizing Maps, WSOM 97, pp. 106-111. Merkl, D. and Rauber, A. (1998) “Cluster connections: A visualization technique to reveal cluster boundaries in self-organizing maps,” Proceedings of the Ninth Italian Workshop on Neural Nets, WIRN 97. Pyle, D. (1999) Data Preparation for Data Mining. San Francisco, CA: Morgan Kaufmann Publishers. Ultsch, A. (1992) “Self-organizing neural networks for knowledge acquisition,” Proceedings of European Conference on Artificial Intelligence, ECAI92, pp. 208-210. Ultsch, A. (1993) “Knowledge extraction from self-organizing neural networks,” in Opitz, O., Lausen, B., and Klar, R., eds., Information and classification, pp. 301-306, Berlin: Springer. Ultsch, A. (1993) “Self-organizing neural networks for visualization and classification,” in Information and Classification-Concepts, Methods and Applications, Berlin: Springer. Ultsch, A. and Vetter, C. (1994) “Self-organizing-feature-maps versus statistical clustering methods: A Benchmark,” University of Marburg, Research Report 0994. Ultsch, A. and Korus, D. (1993) “Automatic acquisition of symbolic knowledge from subsymbolic neural networks,” Proceedings of International Conference on Signal Processing, pp. 1201-1204. Ultsch, A. and Korus, D. (1995) “Integration of neural networks with knowledge-based systems,” Proceedings of the IEEE International Conference on Neural Network. Vellido, A., Lisboa, P. J. G., and Meehan, K. (1999) “Segmentation of the on-line shopping market using neural networks,” Expert systems with applications, Vol. 17, pp. 303-314. Vesanto, J. and Alhoniemi, E. (2000) “Clustering of the self-organizing map,” IEEE Transactions on Neural Networks, Vol. 11, No. 2, pp.586-600. Waller, N. G., Kaiser, H. A., Illian, J. B., and Manry, M. (1998) “A comparison of the classification capabilities of the 1-dimensional kohonen neural network with two partitioning and three hierarchical cluster analysis algorithms,” Psychometrika, Vol. 63, No. 1, pp. 5-22. Witten, I. H. and Frank, E. (2000) Data mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Francisco, CA: Morgan Kaufmann Publishers.

Availability: http://ntur.lib.ntu.edu.tw/handle/246246/51160
http://ntur.lib.ntu.edu.tw/bitstream/246246/51160/1/ntu-93-R89546010-1.pdf

Boosted band ratio feature selection for hyperspectral image classification

Authors: Fu, Zhouyu Caelli, Terry Liu, Nianjun et al.

Source: Proceedings of the 18th International Conference on Pattern Recognition

Index Terms: Keywords: Algorithms; Automation; Classification (of information); Convergence of numerical methods; Image analysis; Large scale systems; Automatic selection; Hyperspectral image classification; Individual ratio features; Kullback-Leibler divergence (KLD); Feature, Conference paper

URL: http://hdl.handle.net/1885/32932
International Conference on Pattern Recognition (ICPR 2006)