Machine Learning
A Constraint-Based Approach
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38 580 Ft
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Product details:
- Edition number 2
- Publisher Morgan Kaufmann
- Date of Publication 5 April 2023
- ISBN 9780323898591
- Binding Paperback
- No. of pages560 pages
- Size 228x152 mm
- Weight 1160 g
- Language English 493
Categories
Long description:
Machine Learning: A Constraint-Based Approach, Second Edition provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that include neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. It draws a path towards deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, such as in fuzzy systems. Special attention is given to deep learning, which nicely fits the constrained-based approach followed in this book.
The book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, including many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.
Table of Contents:
1. The Big Picture
2. Learning Principles
3. Linear-Threshold Machines
4. Kernel Machines
5. Deep Architectures
6. Learning from Constraints
7. Epilogue
8. Answers to selected exercises
Machine Learning: A Constraint-Based Approach
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