Innovations in machine learning theory and applications /
"Machine learning is currently one of the most rapidly growing areas of research in computer science. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapt...
| Other Authors: | Holmes, Dawn E., Jain, L. C., SpringerLink (Online service) |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin ; New York :
Springer,
©2006.
Berlin ; New York : [2006] |
| Physical Description: |
1 online resource (xvi, 274 pages) : illustrations. |
| Series: |
Studies in fuzziness and soft computing ;
v. 194. |
| Subjects: |
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| 245 | 0 | 0 | |a Innovations in machine learning : |b theory and applications / |c Dawn E. Holmes, Lakhmi C. Jain (eds.). |
| 260 | |a Berlin ; |a New York : |b Springer, |c ©2006. | ||
| 264 | 1 | |a Berlin ; |a New York : |b Springer, |c [2006] | |
| 264 | 4 | |c ©2006. | |
| 300 | |a 1 online resource (xvi, 274 pages) : |b illustrations. | ||
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| 490 | 1 | |a Studies in fuzziness and soft computing, |x 1860-0808 ; |v v. 194. | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | 1 | |a "Machine learning is currently one of the most rapidly growing areas of research in computer science. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained." "Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for postgraduate since it shows the direction of current research."--Jacket. | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a A Bayesian Approach to Causal Discovery -- A Tutorial on Learning Causal Influence -- Learning Based Programming -- N-1 Experiments Suffice to Determine the Causal Relations Among N Variables -- Support Vector Inductive Logic Programming -- Neural Probabilistic Language Models -- Computational Grammatical Inference -- On Kernel Target Alignment -- The Structure of Version Space. | |
| 546 | |a English. | ||
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| 650 | 2 | |a Machine Learning. | |
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| 650 | 6 | |a Apprentissage automatique. | |
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| 650 | 0 | 7 | |a Machine learning. |2 cct. |
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