In this paper, we determine the stationary points of Rosenblatt's learning algorithm for a single-layer perceptron and two nonseparable models of the training ...
The authors examine the stationary points of Rosenblatt's algorithm when the data are not linearly separable and derive an expression for the probability of ...
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Rosenblatt's algorithm is a recursive method that is often used to adjust the weights of a single-layer perceptron. It is capable of partitioning the input ...
A single-layer perceptron divides the input signal space into two regions separated by a hyperplane. In many applications, the training signal of the ...
Performance analysis of a single-layer perceptron for a ...
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The authors examine the stationary points of Rosenblatt's algorithm when the data are not linearly separable and derive an expression for the probability of ...
stationary points of Rosenblatt's algorithm when the data are not linearly separable. A system identification model is used to generate the data. An ...
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Rosenblatt's (1985) algorithm is a recursive method used to adjust the weights of a single-layer perceptron. It is capable of partitioning the input signal ...
In this paper, we present an analysis of the stationary points of a single-layer Perceptron that is based on the momentum LMS algorithm, and we illustrate ...
This paper describes two nonseparable data models that can be used to study the convergence properties of perceptron learning algorithms.
Missing: Stationary | Show results with:Stationary
May 30, 2023 · Shynk and N. J. Bershad, Stationary points of a single-layer perceptron for nonseparable data models, Neural Networks 6(2) (1993), 189-202.