Ebook: 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, 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.

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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, 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.

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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, 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.

Content:
Front Matter....Pages I-XVI
A Bayesian Approach to Causal Discovery....Pages 1-28
A Tutorial on Learning Causal Influence....Pages 29-71
Learning Based Programming....Pages 73-95
N-1 Experiments Suffice to Determine the Causal Relations Among N Variables....Pages 97-112
Support Vector Inductive Logic Programming....Pages 113-135
Neural Probabilistic Language Models....Pages 137-186
Computational Grammatical Inference....Pages 187-203
On Kernel Target Alignment....Pages 205-256
The Structure of Version Space....Pages 257-273
Back Matter....Pages 275-276

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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, 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.

Content:
Front Matter....Pages I-XVI
A Bayesian Approach to Causal Discovery....Pages 1-28
A Tutorial on Learning Causal Influence....Pages 29-71
Learning Based Programming....Pages 73-95
N-1 Experiments Suffice to Determine the Causal Relations Among N Variables....Pages 97-112
Support Vector Inductive Logic Programming....Pages 113-135
Neural Probabilistic Language Models....Pages 137-186
Computational Grammatical Inference....Pages 187-203
On Kernel Target Alignment....Pages 205-256
The Structure of Version Space....Pages 257-273
Back Matter....Pages 275-276
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