[[abstract]]A probabilistic perceptron learning algorithm has been proposed here to reduce the computation time of learning. The proposed algorithm is easily programmed and can drastically decrease the time complexity of learning at the expense of only a little accuracy. Experimental results also show this trade-off being worthwhile. Our proposed probabilistic perceptron learning algorithm thus has practical use, especially when the requirement of computational time is critica
A basic neural model for Boolean computation is examined in the context of learning from examples. T...
Abstract We consider the generalization problem for a perceptron with binary synapses, implementing ...
A probabilistic learning system of the type that receives sequential input data and outputs sequence...
This paper presents an easy to use, constructive training algorithm for Probabilistic Neural Network...
Abstract. A perceptron is a linear threshold classifier that separates examples with a hyperplane. I...
Abstract: Within the context of Valiant's protocol for learning, the Perceptron algorithm is sh...
In this paper we propose a Monte Carlo-based learning algorithm for multi-layer perceptron (MLP) whi...
The perceptron algorithm, developed mainly in the machine learning literature, is a simple greedy me...
AbstractLearning real weights for a McCulloch-Pitts neuron is equivalent to linear programming and c...
We extend the geometrical approach to the Perceptron and show that, given n examples, learning is of...
[[abstract]]A parallel perceptron learning algorithm based upon a single-channel broadcast communica...
It was pointed out in this paper that the planar topology of current backpropagation neural network ...
A fundamental task for both biological perception systems and human-engineered agents is to infer un...
There is strong behavioral and physiological evidence that the brain both represents probability dis...
Linear classifiers, that is, classifiers based on linear discriminant functions, are formally intro...
A basic neural model for Boolean computation is examined in the context of learning from examples. T...
Abstract We consider the generalization problem for a perceptron with binary synapses, implementing ...
A probabilistic learning system of the type that receives sequential input data and outputs sequence...
This paper presents an easy to use, constructive training algorithm for Probabilistic Neural Network...
Abstract. A perceptron is a linear threshold classifier that separates examples with a hyperplane. I...
Abstract: Within the context of Valiant's protocol for learning, the Perceptron algorithm is sh...
In this paper we propose a Monte Carlo-based learning algorithm for multi-layer perceptron (MLP) whi...
The perceptron algorithm, developed mainly in the machine learning literature, is a simple greedy me...
AbstractLearning real weights for a McCulloch-Pitts neuron is equivalent to linear programming and c...
We extend the geometrical approach to the Perceptron and show that, given n examples, learning is of...
[[abstract]]A parallel perceptron learning algorithm based upon a single-channel broadcast communica...
It was pointed out in this paper that the planar topology of current backpropagation neural network ...
A fundamental task for both biological perception systems and human-engineered agents is to infer un...
There is strong behavioral and physiological evidence that the brain both represents probability dis...
Linear classifiers, that is, classifiers based on linear discriminant functions, are formally intro...
A basic neural model for Boolean computation is examined in the context of learning from examples. T...
Abstract We consider the generalization problem for a perceptron with binary synapses, implementing ...
A probabilistic learning system of the type that receives sequential input data and outputs sequence...