Machine Learning: A Constraint-Based Approach 1st Edition by Marco Gori (PDF)

Description

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 mannerProvides in-depth coverage of unsupervised and semi-supervised learningIncludes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learningContains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Those who are lucky enough to know Marco Gori personally are well aware of his capacity to talk about virtually any topic in science (and broad surroundings) competently and pleasantly while, above all, always doing so from a non-trivial perspective. Either in front of his students’ audience in a classroom or at conferences, as well as in front of a glass of wine in a relaxed, informal environment, one can enjoy his clear, yet colorful and descriptive speech. For those who do not know him (and are interested in machine learning), this book represents a perfect chance to understand what I mean.Machine learning is an extensive research and application field, whose goals can be achieved by methods which are the outcome of the work of different research groups. This usually causes a perceivable bias, induced by the author’s belonging to one of such communities, when machine learning is dealt with in the scientific literature. By viewing machine learning as a set of methods for solving constraint-satisfaction problems, Marco Gori offers a clear unified, broad and unbiased overview of the field.It is a very comprehensive and well-organized textbook, with an excellent set of non-trivial exercises supporting each chapter, even if possibly not precisely a primer on machine learning. More relevantly, it is a must-read for the researcher or practitioner in machine learning, at any skill level, to whom the original perspective from which the author treats machine learning topics will unveil a world of connections between different methods which are usually hard to detect in more traditional textbooks.Love the unified approach to Machine Learning starting from the environment constraints. Complete and detailed, it exploits Deep Learning with the coinstraint based approach too. It is a good book also for understanding the fundamentals of Machine Learning.Machine learning che tiene esplicitamante conto dei vincoli sulle variabili.

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