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Unsupervised sequence classification
Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop, 2003The 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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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.
Andrei Bacu, Mihaela Dinsoreanu
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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 Feature Learning via Non-parametric Instance Discrimination
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so.
Zhirong Wu +3 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
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Unsupervised time series classification
Signal Processing, 1995Abstract 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
J.J. Rajan, P.J.W. Rayner
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A Study of Unsupervised Classification Techniques for Hyperspectral Datasets
IEEE International Geoscience and Remote Sensing Symposium, 2019This work extensively studies and analyses several unsupervised clustering methods for hyperspectral data. We look at unsupervised classification solutions that accomplish adaptive cluster formation in anticipation for new data discoveries.
Himanshi Yadav +2 more
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Unsupervised HMM classification of F0 curves
Interspeech 2007, 2007This 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 +2 more
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Transformer-based unsupervised contrastive learning for histopathological image classification
Medical Image Anal., 2022Xiyue Wang +7 more
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Multimedia tools and applications, 2022
Viraj A. Gulhane, S. Rode, C. Pande
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Viraj A. Gulhane, S. Rode, C. Pande
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