B. J. Oommen and C. Fothergill, "Fast learning automaton- based image examination and retrieval," The Computer Journal, vol. 36, no. 6, pp. 542-553, 1993. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
String Taxonomy Using Learning Automata - Oommen, Croix (1997) Self-citation (Oommen) (Correct)
....Taxonomy Problem . To our knowledge there is no reported solution to this problem (see footnote on Page 2) In this paper we shall present a learningautomaton based solution to string taxonomy. The solution utilizes the Object Migrating Automaton (OMA) whose power in clustering objects and images [33,35] has been reported. The power of the scheme for string taxonomy has been demonstrated using random strings and garbled versions of string representations of fragments of macromolecules. Keywords : String Taxonomy, String Clustering, Dictionary Partitioning, Syntactic Pattern Recognition. I. ....
....and also to learn the optimal action which a random environment offers. Learning is achieved by interacting with the environment and processing its responses to the chosen actions. LA have various applications including parameter optimization, statistical decision making and telephone routing [27,33,35,36,43]. An excellent book by Narendra and Thathachar [27] contains a review of the families and applications of LA. The learning process of the LA can be described as follows: The LA is offered a set of actions by the environment, and it is constrained to choose one of these actions. On choosing an ....
[Article contains additional citation context not shown here]
B. J. Oommen and C. Fothergill, Fast Learning Automaton-Based Image Examination and Retrieval, The Computer Journal, 36-6:542-553, (1993).
String Taxonomy Using Learning Automata - Oommen, Croix (1994) Self-citation (Oommen) (Correct)
....Taxonomy Problem . To our knowledge there is no reported solution to this problem (see footnote on Page 2) In this paper we shall present a learning automaton based solution to string taxonomy. The solution utilizes the Object Migrating Automaton (OMA) whose power in clustering objects and images [33,35] has been reported. The power of the scheme for string taxonomy has been demonstrated using random strings and garbled versions of string representations of fragments of macromolecules. Keywords : String Taxonomy, String Clustering, Dictionary Partitioning, Syntactic Pattern Recognition. ....
....offers. Learning is achieved by interacting with the environment and String Taxonomy Using Learning Automata Page 5 processing its responses to the actions that are chosen. Such automata have various applications such as parameter optimization, statistical decision making and telephone routing [28,33,35,36,43]. An excellent book on the field by Narendra and Thathachar [28] contains a review of the families and applications of learning automata. The learning process of an automaton can be described as follows: The automaton is offered a set of actions by the environment with which it interacts, and it ....
[Article contains additional citation context not shown here]
B. J. Oommen and C. Fothergill, Fast Learning Automaton-Based Image Examination and Retrieval, The Computer Journal, 36-6:542-553, (1993).
Graph Partitioning Using Learning Automata - Oommen, Croix (1994) (3 citations) Self-citation (Oommen) (Correct)
....the optimal action which a random environment offers. Learning is achieved by interacting with the environment and processing its responses to the actions that are chosen. Such automata have various applications such as parameter optimization, statistical decision making and telephone routing [NT89,OF93,OM88,OVZ92,OZ93]. An excellent book by Narendra and Thathachar [NT89] contains a review of the families and applications of learning automata. The learning process of an automaton can be described as follows: The automaton is offered a set of actions by the environment with which it interacts, and it is ....
....by some amount d 2 . The result is that similar objects will gradually cluster together and dissimilar objects will separate. The best known solution for the EPP is the Object Migrating Automaton (OMA) proposed by Oommen and Ma [OM88] which has already been used in a number of application domains [OF93, OVZ92,OZ93]. The OMA moves all the W objects around within its states as opposed to traditional automata which simply move from one state to another. Thus, when applied to the EPP a solution is not defined by the current state of the OMA; instead it is defined by the entire structure of the automaton. The ....
Oommen, B.J. and Fothergill, C., "Fast Learning Automaton-Based Image Examination and Retrieval", The Computer Journal, Vol. 36, No. 6, 1993, pp. 542-553.
The Bayesian Image Retrieval System, PicHunter.. - Cox, Miller.. (2000) (34 citations) (Correct)
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B. J. Oommen and C. Fothergill, "Fast learning automaton- based image examination and retrieval," The Computer Journal, vol. 36, no. 6, pp. 542-553, 1993.
Describing And Classifying Multimedia Using The.. - Goble, Haul, Bechhofer (1996) (16 citations) (Correct)
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Oommen, BJ and Fothergill C. Fast Learning Automaton-Based Image Examination and Retrieval. The Computer Journal, 36(6), 542--553, 1993.
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