Graph Partitioning Using Learning Automata
IEEE Transactions on Computers
Efficient fast learning automata
Information Sciences—Informatics and Computer Science: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling a student's behavior in a tutorial-like system using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discretized bayesian pursuit --- a new scheme for reinforcement learning
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
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A typical syntactic pattern recognition (PR) problem involves comparing a noisy string with every element of a dictionary, X. The problem of classification can be greatly simplified if the dictionary is partitioned into a set of subdictionaries. In this case, the classification can be hierarchical-the noisy string is first compared to a representative element of each subdictionary and the closest match within the subdictionary is subsequently located. Indeed, the entire problem of subdividing a set of string into subsets where each subset contains “similar” strings has been referred to as the “String Taxonomy Problem”. To our knowledge there is no reported solution to this problem. In this paper we present a learning-automaton based solution to string taxonomy. The solution utilizes the Object Migrating Automaton the power of which in clustering objects and images has been reported. The power of the scheme for string taxonomy has been demonstrated using random string and garbled versions of string representations of fragments of macromolecules