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Unsupervised semantic classification methods

2011 IEEE International Conference on Granular Computing, 2011
A 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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An Approach to Unsupervised Learning Classification

IEEE Transactions on Computers, 1975
In 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, 2020
Traditional 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, Sofia Sheikh, Madalyn Mikkelsen, Vahid Attari, Raymundo Arróyave   +4 more
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A new approach for unsupervised classification

4OR, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hafida Essaqote, Noureddine Zahid, Mohamed Limouri, Abderrahmane Essaid   +3 more
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Unsupervised Twitter Sentiment Classification

Proceedings of the International Conference on Knowledge Management and Information Sharing, 2014
Sentiment 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

2006
We 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, Sriharsha Veeramachaneni, Nicola Polettini, Paolo Avesani   +3 more
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Unsupervised time series classification

Signal Processing, 1995
Abstract In this paper a scheme for unsupervised probabilistic time series classification is detailed. The technique utilizes autocorrelation terms as discriminatory features and employs the Volterra Connectionist Model (VCM) to transform the multi-dimensional feature information of each training vector to a one-dimensional classification space. This
Jebu J. Rajan, Peter J. W. Rayner
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Unsupervised transfer classification

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010
We study the problem of building the classification model for a target class in the absence of any labeled training example for that class. To address this difficult learning problem, we extend the idea of transfer learning by assuming that the following side information is available: (i) a collection of labeled examples belonging to other classes in ...
Tianbao Yang, Rong Jin 0001, Anil K. Jain 0001, Yang Zhou 0033, Wei Tong   +4 more
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Unsupervised HMM classification of F0 curves

Interspeech 2007, 2007
This article describes a new unsupervised methodology to learn F0 classes using HMM models on a syllable basis. A F0 class is represented by a HMM with three emitting states. The clustering algorithm relies on an iterative gaussian splitting and EM retraining process.
Lolive, Damien, Barbot, Nelly, Boëffard, Olivier   +2 more
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Unsupervised sequence classification

Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop, 2003
The authors first introduce a novel approach for unsupervised sequence classification, the competitive sequence learning (CSL) system. The CSL system consists of several extended Kohonen feature maps which are ordered in a hierarchy. The CSL maps develop a representation for subsequences during the training procedure, with an increasing abstraction on ...
J. Kindermann, C. Windheuser
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