A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Method combination for document filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic combination of text classifiers using reliability indicators: models and results
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Combining Multiple Learning Strategies for Effective Cross Validation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Classification Method Study for Automatic Form Class Identification
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Image classification: Classifying distributions of visual features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
International Journal on Document Analysis and Recognition
A Hierarchical Classification Model for Document Categorization
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
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In real applications, a large-scale data set is usually available for a classifier design. The recently proposed Support Cluster Machine (SCM) can deal with such a problem, where data representation is firstly changed with a mixture model such that the classifier works on a component level instead of individual data points. However, it is difficult to decide the proper number of components for designing a successful SCM classifier. In the paper, a hierarchical ensemble SCM (HESCM) is proposed to address the problem. Initially, a hierarchical mixture modeling strategy is used to obtain different levels of mixture models from fine representation to coarse representation. Then, the mixture model in each level is exploited for training SCM. Finally, the learnt models from all the levels are integrated to obtain an ensemble result. Experiments carried on two real large-scale data sets validate the effectiveness of the proposed approach, increasing classification accuracy and stability as well as significantly reducing computational and spatial complexities of a supervised classifier compared to the state-of-the-art classifiers.