Innovations in Machine Learning
Holmes, Dawn E.
Innovations in Machine Learning Theory and Applications / [electronic resource] : edited by Dawn E. Holmes, Lakhmi C. Jain. - XVI, 276 p. online resource. - Studies in Fuzziness and Soft Computing, 194 1434-9922 ; . - Studies in Fuzziness and Soft Computing, 194 .
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.
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.
9783540334866
10.1007/3-540-33486-6 doi
Engineering.
Artificial intelligence.
Engineering mathematics.
Engineering.
Appl.Mathematics/Computational Methods of Engineering.
Artificial Intelligence (incl. Robotics).
TA329-348 TA640-643
519
Innovations in Machine Learning Theory and Applications / [electronic resource] : edited by Dawn E. Holmes, Lakhmi C. Jain. - XVI, 276 p. online resource. - Studies in Fuzziness and Soft Computing, 194 1434-9922 ; . - Studies in Fuzziness and Soft Computing, 194 .
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.
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.
9783540334866
10.1007/3-540-33486-6 doi
Engineering.
Artificial intelligence.
Engineering mathematics.
Engineering.
Appl.Mathematics/Computational Methods of Engineering.
Artificial Intelligence (incl. Robotics).
TA329-348 TA640-643
519