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Clustering for Classification - Reuben Evans
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Reuben Evans:

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2008, ISBN: 9783639031638

[ED: Pappeinband], [PU: Bertrams Print On Demand], - Advances in technology have provided industry with an array of devices for collecting data. The frequency and scale of data collection means that there are now many large datasets being generated. To find patterns in these datasets it would be useful to be able to apply modern methods of classification such as support vector machines. Unfortunately these methods are computationally expensive, quadratic in the number of data points in fact, and so cannot be applied directly. This book proposes a framework whereby a variety of clustering methods can be used to summarise datasets, that is, reduce them to a smaller but still representative dataset so that these advanced methods can be applied. It compares the results of using this framework against using random selection on a large number of classification and regression problems. Results show that the clustered datasets are on average fifty percent smaller than the original datasets without loss of classification accuracy which is significantly better than random selection. They also show that there is no free lunch, for each dataset it is important to choose a clustering method carefully. - Besorgungstitel - vorauss. Lieferzeit 3-5 Tage.., [SC: 0.00]

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Clustering for Classification - Reuben Evans
Vergriffenes Buch, derzeit bei uns nicht verfügbar.
(*)

Reuben Evans:

Clustering for Classification - neues Buch

ISBN: 9783639031638

ID: 44abf666684a05da2b284e1cf4fee6c4

Advances in technology have provided industry with an arrayof devices for collecting data. The frequency and scale of data collection means that there are now many large datasets being generated. To find patterns in these datasets it would be useful to be able to apply modern methods of classification such as support vector machines. Unfortunately these methods are computationally expensive, quadratic in the number of data points in fact, and socannot be applied directly. This book proposes a framework where by a variety of clustering methods can be used to summarise datasets, that is, reduce them to a smaller but still representative dataset so that these advanced methods can be applied. It compares the results of using this framework against using random selection on alarge number of classification and regression problems. Results show thatthe clustered datasets are on average fifty percent smaller than the original datasets without loss of classification accuracy which is significantly better than random selection. They also show that there is no free lunch, for each dataset it is important to choose a clustering method carefully. Buch / Broschur, [PU: VDM Verlag Dr. Müller, Saarbrücken]

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Clustering for Classification - Evans, Reuben
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Evans, Reuben:
Clustering for Classification - Taschenbuch

2008

ISBN: 3639031636

Lieferbar binnen 4-6 Wochen Versandkosten:Versandkostenfrei innerhalb der BRD

ID: 9783639031638

Internationaler Buchtitel. In englischer Sprache. Verlag: VDM Verlag, Paperback, 108 Seiten, L=220mm, B=150mm, H=6mm, Gew.=177gr, [GR: 16390 - HC/Informatik/EDV/Sonstiges], Kartoniert/Broschiert

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Clustering for Classification: Using Standard Clustering Methods to Summarise Datasets with Minimal Loss of Classificat - Reuben Evans
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Clustering for Classification: Using Standard Clustering Methods to Summarise Datasets with Minimal Loss of Classificat - neues Buch

ISBN: 9783639031638

ID: 471683514

NA Loss, VDM Verlag Dr Mueller Aktiengesellschaft &amp amp amp amp amp amp amp Co KG

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Details zum Buch
Clustering for Classification
Autor:

Evans, Reuben

Titel:

Clustering for Classification

ISBN-Nummer:

3639031636

Detailangaben zum Buch - Clustering for Classification


EAN (ISBN-13): 9783639031638
ISBN (ISBN-10): 3639031636
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2008
Herausgeber: VDM Verlag
108 Seiten
Gewicht: 0,177 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 18.01.2007 08:05:04
Buch zuletzt gefunden am 08.05.2014 05:38:00
ISBN/EAN: 3639031636

ISBN - alternative Schreibweisen:
3-639-03163-6, 978-3-639-03163-8

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