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Logic for Learning

Name: Logic for Learning
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783642075537
Price: 18632 HUF
Availability: InStock

Learning Comprehensible Theories from Structured Data

Szerző: John W. Lloyd

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Elérhetőség: Beszállítói készleten

Küldés 5-8 napon belül

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This book provides a systematic approach to knowledge representation, computation, and learning usin...

Információk a könyvről

Szerző

John W. Lloyd

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2010

oldal

257

EAN

9783642075537

ISBN

3642075533

Enbook ID

01652674

Kiadó

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Súly

420

Méretek

234 x 158 x 18

Teljes leírás

This book provides a systematic approach to knowledge representation, computation, and learning using higher-order logic. For those interested in computational logic, it provides a framework for knowledge representation and computation based on higher-order logic, and demonstrates its advantages over more standard approaches based on first-order logic. For those interested in machine learning, the book explains how higher-order logic provides suitable knowledge representation formalisms and hypothesis languages for machine learning applications.This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed, and for those in machine learning no previous knowledge of computational logic is assumed.The logic used throughout the book is a higher-order one, since higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation.§The book should be of interest to researchers in machine learning, especially those who study learning methods for structured data. Throughout, great emphasis is placed on learning comprehensible theories. The book serves as an introduction for computational logicians to machine learning, a particularly interesting and important application area of logic, and also provides a foundation for functional logic programming languages.