Incremental Version-Space Merging: A General Framework for Concept Learning

Name: Incremental Version-Space Merging: A General Framework for Concept Learning
Brand: Springer-Verlag New York Inc.
SKU: 9781461288343
Price: 36174 HUF
Availability: InStock

Szerző: Haym Hirsh

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer-Verlag New York Inc.

Elérhetőség: Beszállítói készleten

Küldés 5-8 napon belül

36 174 Ft

One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly...

Információk a könyvről

Szerző

Haym Hirsh

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2011

oldal

116

EAN

9781461288343

ISBN

1461288347

Enbook ID

02179847

Kiadó

Springer-Verlag New York Inc.

Súly

219

Méretek

155 x 235 x 8

Teljes leírás

One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning. It focuses on a ques tion that is central to understanding how computers might learn: "how can a computer acquire the definition of some general concept by abstracting from specific training instances of the concept?" Although this question of how to automatically generalize from examples has been considered by many researchers over several decades, it remains only partly answered. The approach developed in this book, based on Haym Hirsh's Ph.D. dis sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi mally consistent hypotheses, even in the presence of certain types of incon sistencies in the data. More generally, it provides a framework for integrat ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypotheses under consideration.

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Incremental Version-Space Merging: A General Framework for Concept Learning

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