Add to ORKGThis paper considers the problem of learning optimal discriminant functions for pattern classification. The criterion of optimality is minimising the probability of misclassification. No knowledge of the statistics of the pattern classes is assumed and the given classified sample may be noisy. We present a comprehensive review of algorithms based on the model of cooperating systems of learning automata for this problem. Both finite action set automata and continuous action set automata models are considered. All algorithms presented have rigorous convergence proofs. We also present algorithms that converge to global optimum. Simulation results are presented to illustrate the effectiveness of these techniques based on learning automata
Stochastic automata operating in an unknown random environment have been successfully used in modell...
In many problems of decision making under uncertainty the system has to acquire knowledge of its env...
Stochastic automata operating in an unknown random environment have been successfully used in modell...
This paper considers the problem of learning optimal discriminant functions for pattern classificati...
This paper considers the problem of learning optimal discriminant functions for pattern classificati...
The problem of learning correct decision rules to minimize the probability of misclassification is a...
The problem of learning correct decision rules to minimize the probability of misclassification is a...
The problem of learning correct decision rules to minimize the probability of misclassification is a...
A model made of units of teams of learning automata is developed for the three layer pattern classif...
We consider stochastic automata models of learning systems in this article. Such learning automata s...
We consider stochastic automata models of learning systems in this article. Such learning automata s...
This paper describes new and efficient algorithms for learning deterministic finite automata. Our ap...
Stochastic automata operating in an unknown random environment have been proposed earlier as models ...
Stochastic automata operating in an unknown random environment have been successfully used in modell...
Stochastic automata operating in an unknown random environment have been successfully used in modell...
Stochastic automata operating in an unknown random environment have been successfully used in modell...
In many problems of decision making under uncertainty the system has to acquire knowledge of its env...
Stochastic automata operating in an unknown random environment have been successfully used in modell...
This paper considers the problem of learning optimal discriminant functions for pattern classificati...
This paper considers the problem of learning optimal discriminant functions for pattern classificati...
The problem of learning correct decision rules to minimize the probability of misclassification is a...
The problem of learning correct decision rules to minimize the probability of misclassification is a...
The problem of learning correct decision rules to minimize the probability of misclassification is a...
A model made of units of teams of learning automata is developed for the three layer pattern classif...
We consider stochastic automata models of learning systems in this article. Such learning automata s...
We consider stochastic automata models of learning systems in this article. Such learning automata s...
This paper describes new and efficient algorithms for learning deterministic finite automata. Our ap...
Stochastic automata operating in an unknown random environment have been proposed earlier as models ...
Stochastic automata operating in an unknown random environment have been successfully used in modell...
Stochastic automata operating in an unknown random environment have been successfully used in modell...
Stochastic automata operating in an unknown random environment have been successfully used in modell...
In many problems of decision making under uncertainty the system has to acquire knowledge of its env...
Stochastic automata operating in an unknown random environment have been successfully used in modell...