By Ali Ghorbani (auth.), Ning Zhong, Vic Callaghan, Ali A. Ghorbani, Bin Hu (eds.)
This ebook constitutes the refereed lawsuits of the seventh foreign convention on lively Media expertise, 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 ebook. they're grouped in topcial sections on facts mining and development research in energetic media; energetic human-Web interplay and social media; energetic net intelligence functions; lively multi-agent and community platforms; in addition to expertise intelligence.
Read or Download Active Media Technology: 7th International Conference, AMT 2011, Lanzhou, China, September 7-9, 2011. Proceedings PDF
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Extra resources for Active Media Technology: 7th International Conference, AMT 2011, Lanzhou, China, September 7-9, 2011. Proceedings
231–238. : Combining Pattern Classifiers: Methods and Algorithms. : Should confidence be trusted? : Combining Multiple Feature Selection Methods for Text Categorization by Using Rank-Score Characteristics. In: 21st IEEE International Conference on Tools with Artificial Intelligence, pp. : Fusion Analysis of Information Retrieval Models on Biomedical Collections. : Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction. : Combining multiple scoring systems for target tracking using rank-score characteristics.
PWC1 st Data Bag 1 . . PWCk,1 on k EPP Data Bag m PWCk,m on kst EPP Pk,1 . . Pk,m PWCk PWCi,j: jth classifier of ith pairwise classifier ensemble specialized for ith error-prone pairclass Pi,j: accuracy of jth classifier in PWCi ensembles Fig. 3. The first training phase of the proposed method m PWCi ( x | h) = ∑ wi , j * PWCi , j ( x | h) , h ∈ EPPCi (2) j =1 where x is a test data. The last step of the proposed framework is to combine the results of the main multiclass classifier and those of PWCi.
In this paper, some ensembles of classifiers are first created. The classifiers of each of these ensembles jointly work using majority weighting votes. The results of these ensembles are combined to decide the final vote in a weighted manner. Finally the outputs of these ensembles are heuristically aggregated. The proposed framework is evaluated on a very large scale Persian digit handwritten dataset and the experimental results show the effectiveness of the algorithm. Keywords: Genetic Algorithm, Optical Character Recognition, Pairwise Classifier, Multiclass Classification.