Showing papers on "Unsupervised learning published in 1977"

A quasi-Bayes unsupervised learning procedure for priors (Corresp.)

Abstract: Unsupervised Bayes sequential learning procedures for classification and estimation are often useless in practice because of the amount of computation required. In this paper, a version of a two-class decision problem is considered, and a quasi-Bayes procedure is motivated and defined. The proposed procedure mimics closely the formal Bayes solution while involving only a minimal amount of computation. Convergence properties are established and some numerical illustrations provided. The approach compares favorably with other non-Bayesian learning procedures that have been proposed and can be extended to more general situations.

Knowledge-directed learning

Abstract: A system embodying a knowledge-directed approach to unsupervised learning is examined in this paper. This approach is based on the premise that knowledge of new situations is acquired and interpreted in terms of the previous knowledge brought to the learning situation. In particular, our system is provided with a general characterization of action-oriented competitive games. This frame of reference is used to construct an interpretation for the patterns of human activity that are observed in games of baseball.Multiple levels of knowledge and processing are used to proceed through various levels of description of the observed human behavior. Hypothesis Generation shifts the pattern description from observed physical actions such as "catch" and "run" to inferred goals and causal relationships of the players executing those actions. Hypothesis Generalization abstracts generalized classes of events and schemata that represent concepts such as "hit" and "out". Hypothesis Evaluation closes the loop in the learning process by verifying or rejecting the various hypotheses. Knowledge encoded as schemata direct these processes; there are schemata for inferring competitive and cooperative goals and causal relationships of players.An important aspect of the system is its ability to use acquired knowledge. The multi-level organization facilitates the integration of the new information into the existing knowledge structure. Also, both the initial knowledge and the acquired knowledge are represented uniformly as schemata (production rules). Acquired schemata, then, are available to assist in interpreting and predicting future events. This ability demonstrates the effectiveness of our knowledge-directed approach to learning.

Modified stochastic approximation to enhance unsupervised learning

Abstract: By simple modifications of a decision-directed learning procedure, the regression curves of multidimensional stochastic approximation can be rotated further apart, leading to enhanced convergence properties. Results of a Monte Carlo simulation for a binary hypotheses testing problem are given which illustrates this faster convergence.

FEAST: Feature evaluation and selection technique for deployment in unsupervised nonparametric environments

Abstract: The problem of feature selection in a totally unsupervised, distribution free environment being conceptually ill-defined, the problem has been studied in an artifically evolved pseudosupervised environment. The evolution of such an environment is achieved by formulating a unified approach to the twin problems of feature selection and unsupervised learning. The solution of the latter problem leads to the pseudosupervised environment in which the features are evaluated by employing a multistate-choice automaton model as the feature selector. The methodology developed here is intended to be deployed in conjunction with any one of the numerous recursive schemes of clustering in which the crudely formed initial clusters are refined in a recursive fashion by successively determining the centroids of the different clusters and reallocating the samples to the clusters defined by these centroids. This allocation is carried out on the basis of distance measures (Euclidean or modifications thereof) and is in parallel progress with the feature-evaluation process. The clusters, as formulated at each stage of the recursive process, provide the pseudosupervised environment for the feature selector. The track record of the automaton in terms of probabilities of penalized action provides a measure of the efficiency of the different feature subsets in the unsupervised environment.