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: |
| Summary: |
"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. |
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| Item Description: |
Includes bibliographical references and index. "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. 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. English. University staff and students only. Requires University Computer Account login off-campus. |
| Physical Description: |
1 online resource (xvi, 274 pages) : illustrations. |
| Bibliography: |
Includes bibliographical references and index. |
| ISBN: |
9783540334866 3540334866 3540306099 9783540306092 1280610581 9781280610585 6610610584 9786610610587 |
| ISSN: |
1860-0808 ; |
| Access: |
University staff and students only. Requires University Computer Account login off-campus. |