1 |
V. Cherkassky, F. Mulier, Learning From Data Concepts, Theory, and Methods, John Wiley & Sons, 1998
|
2 |
P. Giudici, Applied Data Mining, Wiley, 2003
|
3 |
J. Han, M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, 2001
|
4 |
S. Haykin, Neural Networks, Prentice Hall, 1999
|
5 |
T. M. Mitchell, Machine Learning, McGraw-Hill, 1997
|
6 |
K. B. Korb, A. E. Nicholson, Bayesian Artificial Intelligence, Chapman & Hall, 2004
|
7 |
V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995
|
8 |
R. A. Johnson, D. W. Wichern, Applied Multivariate Statistical Analysis, Prentice Hall, 1992
|
9 |
A. S. Pandya, R. B. Macy, Pattern Recognition with Neural Networks in C++, IEEE Press, 1995
|
10 |
UCI Machine Learning Repository, http://www1.ics.uci.edu/~mlearn
|
11 |
V. N. Vapnik, "An Overview of Statistical Learning Theory", IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988-999, 1999
DOI
ScienceOn
|
12 |
V. Vapnik, Statistical Learning Theory, John Wiley & Sons, Inc. 1998
|
13 |
T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data mining, Inference, and Prediction, Springer, 2001
|
14 |
L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, Classification and Regression Trees, Wadsworth Inc., 1984
|