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【正文】 atternbased classification ? Other classification methods: lazy learners (KNN, casebased reasoning), geic algorithms, rough set and fuzzy set approaches ? Additional Topics on Classification ? Multiclass classification ? Semisupervised classification ? Active learning ? Transfer learning 55 References (1) ? C. M. Bishop, Neural Networks for Pattern Recognition. Oxford University Press, 1995 ? C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2): 121168, 1998 ? H. Cheng, X. Yan, J. Han, and . Hsu, Discriminative Frequent pattern Analysis for Effective Classification, ICDE39。07 ? H. Cheng, X. Yan, J. Han, and P. S. Yu, Direct Discriminative Pattern Mining for Effective Classification, ICDE39。08 ? N. Cristianini and J. ShaweTaylor, Introduction to Support Vector Machines and Other KernelBased Learning Methods, Cambridge University Press, 2022 ? A. J. Dobson. An Introduction to Generalized Linear Models. Chapman amp。 Hall, 1990 ? G. Dong and J. Li. Efficient mining of emerging patterns: Discovering trends and differences. KDD39。99 56 References (2) ? R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification, 2ed. John Wiley, 2022 ? T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. SpringerVerlag, 2022 ? S. Haykin, Neural Networks and Learning Machines, Prentice Hall, 2022 ? D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian works: The bination of knowledge and statistical data. Machine Learning, 1995. ? V. Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic, MIT Press, 2022 ? W. Li, J. Han, and J. Pei, CMAR: Accurate and Efficient Classification Based on Multiple ClassAssociation Rules, ICDM39。01 ? . Lim, . Loh, and . Shih. A parison of prediction accuracy, plexity, and training time of thirtythree old and new classification algorithms. Machine Learning, 2022 57 References (3) ? B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining, p. 8086, KDD’98. ? T. M. Mitchell. Machine Learning. McGraw Hill, 1997. ? . Rumelhart, and . McClelland, editors, Parallel Distributed Processing, MIT Press, 1986. ? P. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison Wesley, 2022. ? S. M. Weiss and N. Indurkhya. Predictive Data Mining. Man Kaufmann, 1997. ? I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques, 2ed. Man Kaufmann, 2022. ? X. Yin and J. Han. CPAR: Classification based on predictive association rules. SDM39。03 ? H. Yu, J. Yang, and J. Han. Classifying large data sets using SVM with hierarchical clusters. KDD39。03. 2022年 1月 4日星期二 Data Mining: Concepts and Techniques 58 SVM—Introductory Literature ? “ Statistical Learning Theory‖ by Vapnik: extremely hard to understand, containing many errors too. ? C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2), 1998. ? Better than the Vapnik’s book, but still written too hard for introduction, and the examples are so notintuitive ? The book ―An Introduction to Support Vector Machines‖ by N. Cristianini and J. ShaweTaylor ? Also written hard for introduction, but the explanation about the mercer’s theorem is better than above literatures ? The neural work book by Haykins ? Contains one nice chapter of SVM introduction 59 Notes about SVM—Introductory Literature ? “ Statistical Learning Theory‖ by Vapnik: difficult to understand, containing many errors. ? C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2), 1998. ? Easier than Vapnik’s book, but still not introductory level。 the examples are not so intuitive ? The book An Introduction to Support Vector Machines by Cristianini and ShaweTaylor ? Not introductory level, but the explanation about Mercer’s Theorem is better than above literatures ? Neural Networks and Learning Machines by Haykin ? Contains a nice chapter on SVM introduction
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