Results 221 to 230 of about 95,703 (260)

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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Unsupervised Classification of Translated Texts

2015
In our paper we investigate the possibility to use an unsupervised classifier to automatically distinguish between the translated and original novels of a multilingual writer (Vladimir Nabokov) and to determine whether the authorship of a translated document can be achieved.
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Unsupervised Classification

2023
Juan J. Cuadrado-Gallego, Yuri Demchenko
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Clustering and unsupervised classification

2014
Abstract This chapter examines iterative clustering methods including finite mixture modelling based on the EM algorithm. Applications to behavioural segmentation of domestic energy customers, the identification of customer missions in supermarkets, and behaviour-based targeting for mobile network operators are covered.
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Clustering and Unsupervised Classification

1986
The successful application of maximum likelihood classification is dependent upon having delineated correctly the spectral classes in the image data of interest. This is necessary since each class is to be modelled by a normal probability distribution, as discussed in Chap. 8.
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Unsupervised Classification

2018
Stacy A. C. Nelson, Siamak Khorram
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Unsupervised Sentiment Classification

2016
This chapter describes the design and implementation of the unsupervised sentiment classification procedure. The classification procedure consisted of two core components: a bespoke sentiment analysis system developed by the author and the SenticNet sentiment lexicon. The sentiment lexicon acted as the source of sentiment information, and the sentiment
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