Machine Learning: A Constraint-Based Approach

Copertina anteriore
Morgan Kaufmann, 20 nov 2017 - 580 pagine
3 Recensioni

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
 

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Recensione dell'utente - Segnala come inappropriato

Some years ago Marco Gori described to me his idea of constraint-based learning. Not much time was required to convince myself that Marco’s point of view was strongly innovative. I enthusiastically accepted his invitation to work together on some aspects related to this subject and so I could give a small contribution to Marco’s “big picture”. When I first saw the monograph, I was impressed by how much work Marco did in recent years on this topic and how clearly he can now present constraint-based learning. The book looks to me like a great “fresco”, in which a large variety of colours and tones have been put together in such a way as to create an exciting overall view. Despite the novelty of the approach described and the fact that research on this subject is quite recent, the material is presented with the elegance and clarity of a classical, well-established theory. Marco has the capability to guide through the various chapters of his "novel", so that the reader can enjoy the book like an enthusiastic journey in the world of constraints. Each concept and each theorem are presented in such a way that they seem to arise in the most natural way and exactly at the right moment. Although some advanced mathematical tools are used, rigour is achieved avoiding heavy formalism and allowing also less technically-oriented readers to enjoy the book. Summing up, this is an excellent monograph dealing with machine learning from an innovative point of view, which lays the foundations of a new theory with strong impact on machine learning applications. It opens the doors to a wide range of research directions. Definitely, Marco’s work is a “must” for researchers and practitioners in machine learning. 

Recensione dell'utente - Segnala come inappropriato

An excellent book on machine learning, with a rigorous yet not heavy formalism, capable of elaborating a unifying view of this discipline from a novel and interesting perspective. A stimulating reading both for students that want to approach this research field, and for experts that want to gain a better understanding of selected topics. 

Pagine selezionate

Sommario

2 Learning Principles
60
3 Linear Threshold Machines
122
4 Kernel Machines
186
5 Deep Architectures
236
6 Learning and Reasoning With Constraints
340
7 Epilogue
446
8 Answers to Exercises
452
C1 Functionals and Variations
518
C2 Basic Notion on Variations
520
C3 EulerLagrange Equations
523
C4 Variational Problems With Subsidiary Conditions
526
Bibliography
534
Index
552
Back Cover
561
Copyright

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Machine Learning: A Constraint-Based Approach
Marco Gori
Anteprima non disponibile - 2017

Parole e frasi comuni

agent algorithm analysis associated assume backpropagation Basically Boolean functions classic clearly complexity computational condition configurations constraints construct convex convolutional corresponding decision defined dimension discussion domain dual space environment error function examples Exercise expressed feature space feedforward feedforward neural network finite formulation framework given gradient descent graph Hence hidden layer hidden units induction input Interestingly interpretation invariance involved kernel machines Lagrangian Lagrangian multipliers learning algorithms learning tasks Let us consider linear machines linearly-separable loss function machine learning matrix minimization neural network neurons Notice optimization output parameters parsimony patterns perceptron points pointwise polynomial predicates principle problem promptly Prove pseudoinverse quadratic random variable realization recurrent regression regularization representation retina scheme Section sequence softmax solution structure supervised learning Suppose t-norm tion training set vector vertexes weights yields

Informazioni sull'autore (2017)

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.

Informazioni bibliografiche

QR code for Machine Learning
TitoloMachine Learning: A Constraint-Based Approach
AutoreMarco Gori
EditoreMorgan Kaufmann, 2017
ISBN0081006705, 9780081006702
Lunghezza580 pagine
  
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