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|a XX-XxUND
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| 245 |
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0 |
|a MACHINE LEARNING
|c TOM M. MITCHELL
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| 260 |
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|a SINGAPORE
|b MC GRAW HILL
|g 1997
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| 300 |
|
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|a pag. 414, i-xvii
|b figures
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| 504 |
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|a ref. p.p. 388-390, index p.p.394-414
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| 505 |
1 |
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|a Chapter 1 Introduction p.1
|a Chapter 2 Concept Learning and the General-to-Specofic Ordwring p. 20
|a Chapter 3 Decision Tree Learning p. 52
|a Chapter 4 Artificial Neural Networks p. 81
|a Chapter 6Bayesian Learning p. 154
|a Chapter 6 Bayesian Learning p. 154
|a Chapter 7 Computational Learning Theory p. 201
|a Chapter 8 Instance-Based Learning p. 230
|a Chapter 9 Genetic Algorithms p. 249
|a Chapter 10 Learning Sets of Rules p. 274
|a Chapter 11 Analytical Learning p. 307
|a Chapter 12 Combining Inductive and Analyticl Learning p. 334
|a Chapter 13 Reinforcement Learning p.367
|
| 650 |
|
4 |
|a Μηχανική εκμάθηση
|9 113096
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| 700 |
1 |
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|a Mitchell, T. M.
|4 aut
|9 113365
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