Innovations in Machine Learning [electronic resource] : Theory and Applications / edited by Dawn E. Holmes, Lakhmi C. Jain.Material type: TextLanguage: English Series: Studies in Fuzziness and Soft Computing: 194Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006Description: XVI, 276 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540334866Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
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.