Results 211 to 220 of about 95,703 (260)
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An Approach to Unsupervised Learning Classification
IEEE Transactions on Computers, 1975In this correspondence, an approach to unsupervised pattern classifiers is discussed. The classifiers discussed here have the ability of obtaining the consistent estimates of unknown statistics of input patterns without knowing the a priori probability of each category's occurrence where the input patterns are of a mixture distribution.
Riichiro Mizoguchi, Masamichi Shimura
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Microstructure classification in the unsupervised context
Acta Materialia, 2020Traditional microstructure classification requires human annotations provided by a subject matter expert. The requirement of human input is both costly and subjective and cannot keep up with the current volume of experimentally and computationally generated microstructure images. In this work, we develop a framework that is capable of reducing the cost
Courtney Kunselman +4 more
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Unsupervised semantic classification methods
2011 IEEE International Conference on Granular Computing, 2011A current problem in text processing is the inability to make accurate unsupervised semantic classification systems. In this research we study the unsupervised semantic classification problem using several approaches. We find that morphological and semantic hints can be translated into effective rules within semantic classification.
John Gilmer, Jianhua Chen 0003
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A new approach for unsupervised classification
4OR, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hafida Essaqote +3 more
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Unsupervised Twitter Sentiment Classification
Proceedings of the International Conference on Knowledge Management and Information Sharing, 2014Sentiment classification is not a new topic but data sources having different characteristics require customized methods to exploit the hidden existing semantic while minimizing the noise and irrelevant information. Twitter represents a huge pool of data having specific features.
Mihaela Dînsoreanu, Andrei Bacu
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Regularization for Unsupervised Classification on Taxonomies
2006We study unsupervised classification of text documents into a taxonomy of concepts annotated by only a few keywords. Our central claim is that the structure of the taxonomy encapsulates background knowledge that can be exploited to improve classification accuracy.
Diego Sona +3 more
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