Journal ArticleDOI
Unsupervised time series classification
J. J. Rajan,Peter J. W. Rayner +1 more
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TLDR
A scheme for unsupervised probabilistic time series classification is detailed that utilizes autocorrelation terms as discriminatory features and employs the Volterra Connectionist Model to transform the multi-dimensional feature information of each training vector to a one-dimensional classification space.About:
This article is published in Signal Processing.The article was published on 1995-09-01. It has received 13 citations till now. The article focuses on the topics: Linear classifier & Unsupervised learning.read more
Citations
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Patent
Signal interpretation engine
TL;DR: In this paper, a signal interpretation engine apparatus and method is described, including a computer programmed to run a feature expansion module, weight table module, a consolidation module, and a map generation module.
Journal ArticleDOI
Fuzzy Clustering for Data Time Arrays With Inlier and Outlier Time Trajectories
TL;DR: Dynamic fuzzy clustering models for classifying a set of multivariate time trajectories (time series, sequences) are developed by adopting an exploratory approach based on a geometric-algebraic formulation of the data time array and different kinds of dynamic fuzzy clustered models, based on cross sectional and longitudinal aspects, are suggested.
Patent
Drug profiling apparatus and method
TL;DR: In this paper, a method for assessing a condition of an organism having body waves corresponding to states of the organism is proposed, which includes the step of recording signals corresponding to a body wave, output by a portion of the organisms in a first state, to provide a first record.
Journal ArticleDOI
Fuzzy c-means clustering models for multivariate time-varying data: Different approaches
TL;DR: The classification of multivariate time-varying data finds application in several fields, such as economics, finance, marketing research, psychometrics, bioinformatics, medicine, signal processing, and more.
Patent
Petroleum exploration and prediction apparatus and method
Daniel R. Cook,John D. Walther +1 more
TL;DR: In this article, the authors proposed a method for predicting the state of a geological formation by generating a separation key effective to extract a first feature from a signal or signals corresponding to a first state and a second feature, distinct from the first feature, from signals or signals corresponding to a second state.
References
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Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Book
Detection, Estimation, And Modulation Theory
TL;DR: Detection, estimation, and modulation theory, Detection, estimation and modulation theorists, اطلاعات رسانی کشاورزی .
Journal ArticleDOI
Probabilistic neural networks
TL;DR: A probabilistic neural network that can compute nonlinear decision boundaries which approach the Bayes optimal is formed, and a fourlayer neural network of the type proposed can map any input pattern to any number of classifications.