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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.
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Veloso,A.:Demand-Driven Associative (eng)
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Demand-Driven Associative Classification (SpringerBriefs in Computer Science)
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Demand-Driven Associative Classification (SpringerBriefs in Computer Science)
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Demand-Driven Associative Classification (SpringerBriefs in Computer Science)
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Demand-Driven Associative Classification
Publicado por
Springer London Mai 2011, 2011
ISBN 10: 0857295241
ISBN 13: 9780857295248
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 128 pp. Englisch. Nº de ref. del artículo: 9780857295248
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Demand-Driven Associative Classification
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Condición: New. pp. 128. Nº de ref. del artículo: 263581658
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Demand-Driven Associative Classification
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Condición: New. Print on Demand pp. 128 27 Illus. Nº de ref. del artículo: 4266245
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Demand-Driven Associative Classification
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Springer London Ltd, 2011
ISBN 10: 0857295241
ISBN 13: 9780857295248
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Demand-Driven Associative Classification
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
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Condición: New. PRINT ON DEMAND pp. 128. Nº de ref. del artículo: 183581648
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Demand-Driven Associative Classification
Publicado por
Springer London, 2011
ISBN 10: 0857295241
ISBN 13: 9780857295248
Nuevo
Kartoniert / Broschiert
Librería: moluna, Greven, Alemania
Calificación del vendedor: 4 de 5 estrellas
Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. First book only devoted to associative classification, which is an emerging classification strategyThe work puts associative classification algorithms into the existing machine learning theoryThe work lists several successful application sc. Nº de ref. del artículo: 5979337
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