Demand-Driven Associative Classification (SpringerBriefs in Computer Science) - Tapa blanda

Libro 1 de 322: SpringerBriefs in Computer Science

Veloso, Adriano; Meira Jr., Wagner

 
9780857295248: Demand-Driven Associative Classification (SpringerBriefs in Computer Science)
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ISBN 10: 0857295241 ISBN 13: 9780857295248
Editorial: Springer, 2011

Sinopsis

The ultimate goal of machines is to help humans to solve problems.
Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.

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Reseña del editor

This volume focuses on a major machine learning task known as classification. Some classification problems are hard to solve, but this book shows that they can be decomposed into much simpler sub-problems.

Reseña del editor

The ultimate goal of machines is to help humans to solve problems.
Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2011
Idioma
Inglés
ISBN 10 
0857295241
ISBN 13 
9780857295248
Encuadernación
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Número de páginas
128
Contacto del fabricante
no disponible
Persona responsable
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Hamburg
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9780857295262: Demand-Driven Associative Classification

Edición Destacada

ISBN 10:  0857295268 ISBN 13:  9780857295262
Editorial: Springer, 2011
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