Multiple Classifier Systems : First International Workshop, MCS 2000 Cagliari, Italy, June 21-23, 2000 Proceedings
Language: English
Published by Springer, Springer, 2000
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Druck auf Anfrage Neuware - Printed after ordering - Many theoretical and experimental studies have shown that a multiple classi er system is an e ective technique for reducing prediction errors [9,10,11,20,19]. These studies identify mainly three elements that characterize a set of cl- si ers: Therepresentationoftheinput(whateachindividualclassi erreceivesby wayofinput). Thearchitectureoftheindividualclassi ers(algorithmsandparametri- tion). The way to cause these classi ers to take a decision together. Itcanbeassumedthatacombinationmethodise cientifeachindividualcl- si ermakeserrors inadi erentway ,sothatitcanbeexpectedthatmostofthe classi ers can correct the mistakes that an individual one does [1,19]. The term weak classi ers refers to classi ers whose capacity has been reduced in some way so as to increase their prediction diversity. Either their internal architecture issimple(e.g.,theyusemono-layerperceptronsinsteadofmoresophisticated neural networks), or they are prevented from using all the information available. Sinceeachclassi erseesdi erentsectionsofthelearningset,theerrorcorre- tion among them is reduced. It has been shown that the majority vote is the beststrategyiftheerrorsamongtheclassi ersarenotcorrelated.Moreover, in real applications, the majority vote also appears to be as e cient as more sophisticated decision rules [2,13]. Onemethodofgeneratingadiversesetofclassi ersistoupsetsomeaspect ofthetraininginputofwhichtheclassi erisrather unstable. In the present paper,westudytwodistinctwaystocreatesuchweakenedclassi ers;i.e.learning set resampling (using the Bagging approach [5]), and random feature subset selection (using MFS , a Multiple Feature Subsets approach [3]). Other recent and similar techniques are not discussed here but are also based on modi cations to the training and/or the feature set [7,8,12,21].…
Seller Inventory # 9783540677048
- Title
- Multiple Classifier Systems : First International Workshop, MCS 2000 Cagliari, Italy, June 21-23, 2000 Proceedings
- Author
- Josef Kittler
- Publisher
- Springer, Springer
- Publication year
- 2000
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3540677046
- ISBN 13
- 9783540677048
- Item weight
- 639 grams
- Dimensions
- 235x155x23 mm
"Synopsis" may belong to another edition of this title.
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