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MACHINE LEARNING: A CONSTRAINT-BASED APPROACH

Information Technology

Computing & Information Technology

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

<P>This book provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. It book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book.</P>

Machine Learning

COLFR

COLSO

COLJR

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Dedication.
Contents.
Preface.
Notes on the Exercises.
1: The Big Picture.
2: Learning Principles.
3: Linear Threshold Machines.
4: Kernel Machines.
5: Deep Architectures.
6: Learning and Reasoning with Constraints.
7: Epilogue.
8: Answers to Exercises.
Appendix A Constrained Optimization in Finite Dimensions.
Appendix B Regularization Operators.
Appendix C Calculus of Variations.
Appendix D Index to Notation.
Bibliography.
Index.

Machine Learning.css-1p8rvzy{position:absolute;padding-left:8px;}

Machine Learning