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: |
Table of Contents:
- 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.