PDF BookMultiple Classifier Systems First International Workshop MCS 2000 Cagliari Italy June 21-23 2000 Proceedings (Lecture Notes in Computer Science)

[Free.FVeL] Multiple Classifier Systems First International Workshop MCS 2000 Cagliari Italy June 21-23 2000 Proceedings (Lecture Notes in Computer Science)

[Free.FVeL] Multiple Classifier Systems First International Workshop MCS 2000 Cagliari Italy June 21-23 2000 Proceedings (Lecture Notes in Computer Science)

You can download in the form of an ebook: pdf, kindle ebook, ms word here and more softfile type. [Free.FVeL] Multiple Classifier Systems First International Workshop MCS 2000 Cagliari Italy June 21-23 2000 Proceedings (Lecture Notes in Computer Science), this is a great books that I think are not only fun to read but also very educational.
Book Details :
Published on: 2008-06-13
Released on:
Original language: English

Many theoretical and experimental studies have shown that a multiple classier system is an eective technique for reducing prediction errors [9,10,11,20,19]. These studies identify mainly three elements that characterize a set of cl- siers: -Therepresentationoftheinput(whateachindividualclassierreceivesby wayofinput). -Thearchitectureoftheindividualclassiers(algorithmsandparametri- tion). - The way to cause these classiers to take a decision together. Itcanbeassumedthatacombinationmethodisecientifeachindividualcl- siermakeserrors'inadierentway',sothatitcanbeexpectedthatmostofthe classiers can correct the mistakes that an individual one does [1,19]. The term 'weak classiers' refers to classiers 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. Sinceeachclassierseesdierentsectionsofthelearningset,theerrorcorre- tion among them is reduced. It has been shown that the majority vote is the beststrategyiftheerrorsamongtheclassiersarenotcorrelated.Moreover, in real applications, the majority vote also appears to be as ecient as more sophisticated decision rules [2,13]. Onemethodofgeneratingadiversesetofclassiersistoupsetsomeaspect ofthetraininginputofwhichtheclassierisrather unstable. In the present paper,westudytwodistinctwaystocreatesuchweakenedclassiers;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 modications to the training and/or the feature set [7,8,12,21]. PageInsider - Information about all domains Own a website? Manage your page to keep your users updated View some of our premium pages: google.com. yelp.com. yahoo.com. microsoft.com. Upgrade to a Premium Page
Free BookMen Women Limited Edition Two Volumes Signed