Auto-kernel using multilayer perceptron

• Autorzy: Cheng W. C.
• Języki publikacji: EN

Abstrakty

EN

This work presents a constructive method to train the multilayer perceptron layer after layer successively and to accomplish the kernel used in the support vector machine. Data in different classes will be trained to map to distant points in each layer. This will ease the mapping of the next layer. A perfect mapping kernel can be accomplished successively. Those distant mapped points can be discriminated easily by a single perceptron.

Słowa kluczowe

EN

kernel function; support vector machine (SVM); multilayer perceptron

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2012
• Tom: Vol. 6, nr 2
• Strony: 60--71
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Cheng W. C.

• Institute of Statistical Science, Academia Sinica, Taiwan, Republic of China,

Bibliografia

• [1] B. E. Boser, I M. Guyon, and V. N. Vapnik „A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pages 144–152, 1992.
• [2] C.-C. Chang and C.-J. Lin. Libsvm : a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm, 2001.
• [3] W.-C. Cheng and C.-Y. Liou. Manifold construction using the multilayer perceptron. In Lecture Notes In Computer Science, volume 5163, Part I, pages 119–127, 2008.
• [4] W.-C. Cheng and C.-Y. Liou. Linear replicator in kernel space. Lecture Notes in Computer Science, 6064, Part II:75–82, 2010.
• [5] R.A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7 Part II:179–188, 1936.
• [6] T. Kohonen. Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43:59–69, 1982.
• [7] O. Krejcar, D. Janckulik, and L. Motalova. Complex biomedical system with biotelemetric monitoring of life functions. In Proceedings of the IEEE Eurocon, pages 138–141, 2009.
• [8] O. Krejcar, D. Janckulik, and L. Motalova. Complex biomedical system with mobile clients. In Proceedings of the World Congress on Medical Physics and Biomedical Engineering, volume 25/5, pages 141–144. Springer, Munich, 2009. Auto-kernel using multilayer perceptron 71
• [9] C.-Y. Liou, H.-T. Chen, and J.-C. Huang. Separation of internal representations of the hidden layer. In Proceedings of the 2000 International Computer Symposium, pages 26–34, 2000.
• [10] C.-Y. Liou and W.-C. Cheng. Resolving hidden representations. In Lecture Notes in Computer Science, volume 4985, Part II, pages 254–263. Springer, Heidelberg, 2008.
• [11] C.-Y. Liou and W.-C. Cheng. Forced accretion and assimilation based on self-organizing neural network. In Self Organizing Maps - Applications and Novel Algorithm Design, pages 683–702, 2011.
• [12] C.-Y. Liou and W.-J. Yu. Initializing the weights in multilayer network with quadratic sigmoid function. In Proceedings of the International Conference on Neural Information Processing, pages 1387–1392, 1994.
• [13] C.-Y. Liou and W.-J. Yu. Ambiguous binary representation in multilayer neural network. In Proceedings of International Conference on Neural Networks, volume 1, pages 379–384, 1995.
• [14] M.A. Little, P.E. McSharry, S.J. Roberts, D.A.E. Costello, and I.M. Moroz. Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMedical Engineering OnLine, 6:23, 2007.
• [15] J. Mercer. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society A, 209:415V446, 1909.
• [16] L. Pearson. On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(11):559–572, 1901.
• [17] D.E. Rumelhart, G.E. Hinton, and R.J. Williams. Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume 1, pages 318–362. Cambridge, MA: MIT Press, 1986.
• [18] Bernhard Scholkopf, Alexander Smola, and Klaus-Robert Muller. Nonlinear component analysis as a kernel eigenvalue problem. Psychometrika, 10:1299–1319, 1998.
• [19] Vladimir Vapnik. The nature of statistical learning theory. In Information Science and Statistics, 2000.
• [20] W.H. Wolberg and O.L. Mangasarian. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In Proceedings of the National Academy of Sciences, volume 87, pages 9193–9196, 1990.