Download Active Media Technology: 7th International Conference, AMT by Ali Ghorbani (auth.), Ning Zhong, Vic Callaghan, Ali A. PDF

By Ali Ghorbani (auth.), Ning Zhong, Vic Callaghan, Ali A. Ghorbani, Bin Hu (eds.)

ISBN-10: 3642236200

ISBN-13: 9783642236204

This ebook constitutes the refereed lawsuits of the seventh overseas convention on lively Media know-how, AMT 2011, held in Lanzhou, China, in September 2011. The 30 revised complete papers and six keynote talks have been conscientiously reviewed and chosen for inclusion within the booklet. they're grouped in topcial sections on facts mining and development research in lively media; energetic human-Web interplay and social media; lively internet intelligence functions; lively multi-agent and community structures; in addition to know-how intelligence.

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Extra info for Active Media Technology: 7th International Conference, AMT 2011, Lanzhou, China, September 7-9, 2011. Proceedings

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After that, a number of pairwise classifiers are employed to reinforce the drawbacks of the main classifier in those error-prone regions. A simple heuristic is used to aggregate their outputs. A Heuristic Classifier Ensemble for Huge Datasets 33 At the first step, a multiclass classifier is trained on all train data. Then, using the results of this classifier on the validation data, confusion matrix is obtained. This matrix contains important information about the functionalities of classifiers in the dataset localities.

This feature of our proposed method makes it applicable for problems with a large number of classes. The experiments show the effectiveness of this method. Also we reached to very good results in Persian handwritten digit recognition which is a very large dataset. References 1. : Bagging Predictors. Journal of Machine Learning 24(2), 123–140 (1996) 2. : Creation of classifier ensembles for handwritten word recognition using feature selection algorithms. IWFHR 2002 (January 15, 2002) 3. : Neural Networks, a comprehensive foundation, 2nd edn.

Also assume that the number of selected EPPC is denoted by k. After determining the mostly erroneous pair-classes, or EPPCs, a set of m ensembles of binary classifiers is to be trained to jointly, as an ensemble of binary classifiers, reinforce the main multiclass classifier in the region of each EPPC. So as it can be inferred, it is necessary to train k ensembles of m binary classifiers. Assume that the ensemble which is to reinforce the main multiclass classifier in the region of EPPCi is denoted by PWCi.

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