Journal Article10.1016/0031-3203(76)90024-8
Learning with probabilistic labeling
Toshio Imai,Masamichi Shimura +1 more
6
TL;DR: The 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, and is computationally feasible to identify a finite mixture.
read more
About: This article is published in Pattern Recognition. The article was published on 01 Jan 1976. The article focuses on the topics: Unsupervised learning & Parametric statistics.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Pattern Recognition and Image Processing
King-Sun Fu,Rosenfeld +1 more
TL;DR: A very brief survey of recent developments in basic pattern recognition and image processing techniques is presented.
188
A fuzzy approximation scheme for sequential learning in pattern recognition
B Bharathi Devi,V. V. S. Sarma +1 more
- 01 Sep 1986
TL;DR: An adaptive learning scheme, based on a fuzzy approximation to the gradient descent method for training a pattern classifier using unlabeled samples, is described and an inductive entropy measure is defined in terms of induced possibility distribution to measure the extent of learning.
19
Learning to recognize patterns with a probabilistic teacher
TL;DR: Weak and strong Bayes risk consistency of the procedures is shown and examples of procedures using the kernel, the nearest neighbor and the orthogonal series estimates are given.
6
An Experimental Study of Some Algorithms for Unsupervised Learning
H. Niemann,G. Sagerer +1 more
TL;DR: It turns out that the iterative solution of the maximum likelihood equations has the best properties among the three approaches, but even this one fails to yield satisfactory results if the number of unknown parameters becomes large, as is usually the case in realistic problems of pattern recognition.
5
References
Probability of error of some adaptive pattern-recognition machines
TL;DR: It is shown that with probability one, the machine converges to the optimal detector for the unknown pattern, that the asymptotic decision function statistics are Gaussian, and that the central-limit theorem can be invoked to calculate the approximate probability of error at any stage of convergence.
700
Learning to recognize patterns without a teacher
TL;DR: This paper presents a model suitable for many problems and evolves a solution in the form of a machine that "learns" to solve the problem without external aid, said to "learn without a teacher".
162
A note on learning for Gaussian properties
TL;DR: By employing a Bayesian approach to the analysis of learning the probability distribution of property vectors, an estimation likelihood computation scheme for the general Gaussian distribution (quadratic adaptive decision surface) is shown optimum.
79
A note on the iterative application of Bayes' rule
TL;DR: Alterations in probability densities produced by iterative application of Bayes' rule are analyzed and the theory is applied to find a class of sequential Bayes estimators for a Gaussian covariance matrix and to treat a variety of adaptive "Bayesian learning" schemes in a unified manner.
73
Joint detection, estimation and system identification
TL;DR: It has been shown that system identification is equivalent to multihypothesis testing, with a continuum or finite sequence of hypotheses, respectively, for continuous or finite discrete range of θ .
63