Multiple Classifier Systems First (9 results)
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Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. K 2000-07-01, 2000
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Multiple Classifier Systems : First International Workshop, McS 2000, Cagliari, Italy, June 2000 : Proceedings
Kittler, Josef (EDT); Roli, Fabio (EDT); International Workshop on Multiple Classifier Systems 2000 Cagliari (COR)
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Multiple Classifier Systems : First International Workshop, McS 2000, Cagliari, Italy, June 2000 : Proceedings
Kittler, Josef (EDT); Roli, Fabio (EDT); International Workshop on Multiple Classifier Systems 2000 Cagliari (COR)
- Softcover
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Taschenbuch. Condition: Neu. 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: There…presentationoftheinput(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].
Multiple Classifier Systems : First International Workshop, McS 2000, Cagliari, Italy, June 2000 : Proceedings
Kittler, Josef (EDT); Roli, Fabio (EDT); International Workshop on Multiple Classifier Systems 2000 Cagliari (COR)
- Softcover
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Multiple Classifier Systems : First International Workshop, McS 2000, Cagliari, Italy, June 2000 : Proceedings
Kittler, Josef (EDT); Roli, Fabio (EDT); International Workshop on Multiple Classifier Systems 2000 Cagliari (COR)
- Softcover
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Taschenbuch. Condition: Neu. Multiple Classifier Systems | First International Workshop, MCS 2000 Cagliari, Italy, June 21-23, 2000 Proceedings | Josef Kittler (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2000 | Springer | EAN 9783540677048 | Verantwortliche Person für die EU: Springer Nature Customer Service…Center GmbH, Europaplatz 3, 69115 Heidelberg, productsafety[at]springernature[dot]com | Anbieter: preigu Print on Demand.
