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

Name: Incremental Version-Space Merging: A General Framework for Concept Learning
Brand: Springer, Berlin
SKU: 9780792391197
Price: 2286 CZK
Availability: InStock

Autor: Haym Hirsh

Jazyk:

Vazba: Pevná

Vydavatel: Springer, Berlin

Dostupnost: Skladem u dodavatele

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2 286 Kč

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

Informace o knize

Autor

Haym Hirsh

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

1990

Stránek

116

EAN

9780792391197

ISBN

0792391195

Enbook ID

01398040

Vydavatel

Springer, Berlin

Hmotnost

372

Rozměry

156 x 234 x 9

Kompletní popis

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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