Machine Learning: A Constraint-Based Approach 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.
The 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.
This book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, and includes 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.
- Presents fundamental machine learning concepts, such as neural networks and kernel machines in a unified manner
- Provides in-depth coverage of unsupervised and semi-supervised learning
- Includes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learning
- Contains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex
Upper level undergraduate and graduate students taking a machine learning course in computer science departments and professionals involved in relevant areas of artificial intelligence
1. The Big Picture
2. Learning Principles
3. Linear-Threshold Machines
4. Feedforward Neural Networks
5. Kernel Machines
6. Constraints on Data
7. Constraints on Tasks
9. Answers to selected exercises
1. Constrained optimization
2. Regularization operators
3. Calculus of variations
4. Regularization operators and kernel machines
5. The SBRS software simulator
- No. of pages:
- © Morgan Kaufmann 2018
- 8th December 2017
- Morgan Kaufmann
- Paperback ISBN:
Professor Gori's research interests are in the field of artificial intelligence, with emphasis on machine learning and game playing. He is a co-author of the book “Web Dragons: Inside the myths of search engines technologies,” Morgan Kauffman (Elsevier), 2007. He was the Chairman of the Italian Chapter of the IEEE Computational Intelligence Society, and the President of the Italian Association for Artificial Intelligence. He is in the list of top Italian scientists kept by VIAAcademy
(http://www.topitalianscientists.org/top_italian_scientists.aspx). Dr. Gori is a fellow of the IEEE, ECCAI, and IAPR.
Department of Information Engineering and Mathematics, University of Siena, Italy