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Machine Learning
Marco Gori
Alessandro Betti
Stefano Melacci
其他書名
A Constraint-Based Approach
出版
Elsevier
, 2023-03-01
主題
Computers / Artificial Intelligence / General
Computers / Artificial Intelligence / Expert Systems
Computers / Database Administration & Management
Computers / Management Information Systems
Mathematics / Applied
Computers / Artificial Intelligence / Computer Vision & Pattern Recognition
Computers / Data Science / Neural Networks
Computers / Computer Science
Mathematics / Discrete Mathematics
Computers / Programming / Algorithms
Computers / Information Theory
Language Arts & Disciplines / Library & Information Science / Archives & Special Libraries
ISBN
032398469X
9780323984690
URL
http://books.google.com.hk/books?id=s6CSEAAAQBAJ&hl=&source=gbs_api
EBook
SAMPLE
註釋
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.
Presents, in a unified manner, fundamental machine learning concepts, such as neural networks and kernel machines
Provides in-depth coverage of unsupervised and semi-supervised learning, with new content in hot growth areas such as deep learning
Includes a software simulator for kernel machines and learning from constraints that also covers exercises to facilitate learning
Contains hundreds of solved examples and exercises chosen particularly for their progression of difficulty from simple to complex
Supported by a free, downloadable companion book designed to facilitate students’ acquisition of experimental skills