Unsupervised learning through symbolic clustering
Pattern Recognition Letters
Brief paper: Random sampling approach to state estimation in switching environments
Automatica (Journal of IFAC)
Brief paper: Detection and estimation for abruptly changing systems
Automatica (Journal of IFAC)
A semi-supervised approach to modeling web search satisfaction
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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The Bayesian learning scheme is computationally infeasible for most of the unsupervised learning problems. This paper suggests a learning scheme, "learning with a probabilistic teacher," which works with unclassified samples and is computationally feasible for many practical problems. In this scheme a sample is probabilistically assigned with a class with appropriate probabilities computed using all the information available: Then the sample is used in learning the parameter values given this assignment of the class. The convergence of the scheme is established and a comparison with the best linear estimator is presented.