LEADER 00000nam  2200361   4500 
001    AAI3417432 
005    20111017084403.5 
008    111017s2010    ||||||||||||||||| ||eng d 
020    9781124147970 
035    (UMI)AAI3417432 
040    UMI|cUMI 
100 1  Ye, Qiang 
245 10 Competitive learning neural network ensemble weighted by 
       predicted performance 
300    88 p 
500    Source: Dissertation Abstracts International, Volume: 71-
       08, Section: A, page: 2690 
500    Adviser: Paul Munro 
502    Thesis (Ph.D.)--University of Pittsburgh, 2010 
520    Ensemble approaches have been shown to enhance 
       classification by combining the outputs from a set of 
       voting classifiers. Diversity in error patterns among base
       classifiers promotes ensemble performance. Multi-task 
       learning is an important characteristic for Neural Network
       classifiers. Introducing a secondary output unit that 
       receives different training signals for base networks in 
       an ensemble can effectively promote diversity and improve 
       ensemble performance. Here a Competitive Learning Neural 
       Network Ensemble is proposed where a secondary output unit
       predicts the classification performance of the primary 
       output unit in each base network. The networks compete 
       with each other on the basis of classification performance
       and partition the stimulus space. The secondary units 
       adaptively receive different training signals depending on
       the competition. As the result, each base network develops
       "preference" over different regions of the stimulus space 
       as indicated by their secondary unit outputs. To form an 
       ensemble decision, all base networks' primary unit outputs
       are combined and weighted according to the secondary unit 
       outputs. The effectiveness of the proposed approach is 
       demonstrated with the experiments on one real-world and 
       four artificial classification problems 
520    Keywords: ensemble, diversity, neural networks, 
       competitive learning, multi-task learning, bias and 
       variance, classification 
590    School code: 0178 
650  4 Information Science 
650  4 Artificial Intelligence 
650  4 Computer Science 
690    0723 
690    0800 
690    0984 
710 2  University of Pittsburgh 
773 0  |tDissertation Abstracts International|g71-08A 
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/
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