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Learning with probabilistic labeling

Authors
Journal
Pattern Recognition
0031-3203
Publisher
Elsevier
Publication Date
Volume
8
Issue
1
Identifiers
DOI: 10.1016/0031-3203(76)90024-8
Keywords
  • Unsupervised Learning
  • Pattern Recognition
  • Probabilistic Labeling
  • Mixture Distribution
  • Parametric Methods
Disciplines
  • Computer Science

Abstract

Abstract A nonsupervised parametric learning model using a randomized labeling procedure is discussed. Our model is an extension of the Agrawala's model and is applicable even in the case where the probability of occurrence of each category is unknown. Furthermore, the method proposed here is computationally feasible to identify a finite mixture. The learning algorithm for multivariate normal distribution is presented in this paper.

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