Abstract
Inspired by kernel matching pursuit (KMP) and support vector machines (SVMs), we propose a novel classification algorithm: kernel matching reduction algorithm (KMRA). This method selects all training examples to construct a kernel-based functions dictionary. Then redundant functions are removed iteratively from the dictionary, according to their weights magnitudes, which are determined by linear support vector machines (SVMs). During the reduction process, the parameters of the functions in the dictionary can be adjusted dynamically. Similarities and differences between KMRA and several other machine learning algorithms are also addressed. Experimental results show KMRA can have sparser solutions than SVMs, and can still obtain comparable classification accuracies to SVMs.
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References
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Berlin (1995)
Vincent, P., Bengio, Y.: Kernel Matching Pursuit. Mach. Learn. 48(1), 165–187 (2002)
Popovici, V., Bengio, S., Thiran, J.P.: Kernel Matching Pursuit for Large Datasets. Pattern Recogn. 38, 2385–2390 (2005)
Moody, T.J., Darken, C.J.: Fast Learning in Networks of Locally Tuned Processing Units. Neural Comput. 1, 151–160 (1989)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene Selection for Cancer Classification Using Support Vector Machines. Mach. learn. 46, 389–422 (2002)
Mladenić, D., Brank, J., Grobelnik, M., Milic-Frayling, N.: Feature Selection Using Linear Classifier Weights: Interaction with Classification Models. In: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 234–241. ACM Press, New York (2004)
Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Zhang, L., Zhou, W., Jiao, L.: Hidden Space Support Vector Machines. IEEE Trans. Neural Networ. 15(6), 1424–1434 (2004)
Blake, C., Keogh, E., Merz, C. J.: UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/~mlearn/MLRepository.html
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification, ftp://ncc.up.pt/pub/statlog
Chang, C.C., Lin, C.J.: LIBSVM: a Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libSVM
Gou, S., Li, Q., Zhang, X.: A New Dictionary Learning Method for Kernel Matching Pursuit. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS (LNAI), vol. 4223, pp. 776–779. Springer, Heidelberg (2006)
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Li, J., Deng, X. (2008). Kernel Matching Reduction Algorithms for Classification. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_76
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DOI: https://doi.org/10.1007/978-3-540-79721-0_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79720-3
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