Innovations in Machine Learning Theory and Applications /
Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neura...
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| Format: | Electrònic eBook |
| Idioma: | English |
| Publicat: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2006.
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| Edició: | 1st ed. 2006. |
| Periòdiques: | Studies in Fuzziness and Soft Computing,
194 |
| Matèries: | |
| Accés en línia: | https://doi.org/10.1007/3-540-33486-6 |
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Taula de continguts:
- 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.