參考文獻

陸汝鈐.（1996）.人工智能（下冊）.科學(xué)出版社，北京。

周志華.（2007）.“機器學(xué)習(xí)與數(shù)據(jù)挖掘.”中國計算機學(xué)會通訊，3（12）：35–44。

李航.（2012）.統(tǒng)計學(xué)習(xí)方法.清華大學(xué)出版社，北京。

Alpaydin, E. （2004）. Introduction to Machine Learning. MIT Press, Cambridge, MA.

Asmis, E. （1984）. Epicurus' Scientific Method. Cornell University Press, Ithaca, NY.

Bishop, C. M. （2006）. Pattern Recognition and Machine Learning. Springer, New York, NY.

Blumer, A., A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. （1996）. “Occam's razor.” Information Processing Letters, 24（6）：377–380.

Carbonell, J. G., ed. （1990）. Machine Learning：Paradigms and Methods. MIT Press, Cambridge, MA.

Cohen, P. R. and E. A. Feigenbaum, eds. （1983）. The Handbook of Artificial Intelligence, volume 3. William Kaufmann, New York, NY.

Dietterich, T. G. （1997）. “Machine learning research：Four current directions.” AI Magazine, 18（4）：97–136.

Domingos, P. （1999）. “The role of Occam’s razor in knowledge discovery.” Data Mining and Knowledge Discovery, 3（4）：409–425.

Duda, R. O., P. E. Hart, and D. G. Stork. （2001）. Pattern Classification, 2nd edition. John Wiley & Sons, New York, NY.

Flach, P. （2012）. Machine Learning：The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, Cambridge, UK.

Hand, D., H. Mannila, and P. Smyth. （2001）. Principles of Data Mining. MIT Press, Cambridge, MA.

Hastie, T., R. Tibshirani, and J. Friedman. （2009）. The Elements of Statistical Learning, 2nd edition. Springer, New York, NY.

Hunt, E. G. and D. I. Hovland. （1963）. “Programming a model of human concept formation.” In Computers and Thought （E. Feigenbaum and J. Feldman, eds.）, 310–325, McGraw Hill, New York, NY.

Kanerva, P. （1988）. Sparse Distributed Memory. MIT Press, Cambridge, MA.

Michalski, R. S., J. G. Carbonell, and T. M. Mitchell, eds. （1983）. Machine Learning：An Artificial Intelligence Approach. Tioga, Palo Alto, CA.

Mitchell, T. （1997）. Machine Learning. McGraw Hill, New York, NY.

Mitchell, T. M. （1977）. “Version spaces：A candidate elimination approach to rule learning.” In Proceedings of the 5th International Joint Conference on Artificial Intelligence （IJCAI）, 305–310, Cambridge, MA.

Mjolsness, E. and D. DeCoste. （2001）. “Machine learning for science：State of the art and future prospects.” Science, 293（5537）：2051–2055.

Pan, S. J. and Q. Yang. （2010）. “A survey of transfer learning.” IEEE Transactions on Knowledge and Data Engineering, 22（10）：1345–1359.

Shalev-Shwartz, S. and S. Ben-David. （2014）. Understanding Machine Learning. Cambridge University Press, Cambridge, UK.

Simon, H. A. and G. Lea. （1974）. “Problem solving and rule induction：A unified view.” In Knowledge and Cognition （L. W. Gregg, ed.）, 105–127, Erlbaum, New York, NY.

Vapnik, V. N. （1998）. Statistical Learning Theory. Wiley, New York, NY.

Webb, G. I. （1996）. “Further experimental evidence against the utility of Occam’s razor.” Journal of Artificial Intelligence Research, 43：397–417.

Winston, P. H. （1970）. “Learning structural descriptions from examples.” Technical Report AI-TR-231, AI Lab, MIT, Cambridge, MA.

Witten, I. H., E. Frank, and M. A. Hall. （2011）. Data Mining：Practical Machine Learing Tools and Techniques, 3rd edition. Elsevier, Burlington, MA.

Wolpert, D. H. （1996）. “The lack of a priori distinctions between learning algorithms.” Neural Computation, 8（7）：1341–1390.

Wolpert, D. H. and W. G. Macready. （1995）. “No free lunch theorems for search.” Technical Report SFI-TR-05-010, Santa Fe Institute, Sante Fe, NM.

Zhou, Z.-H. （2003）. “Three perspectives of data mining.” Artificial Intelligence, 143（1）：139–146.