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Abstract: A structure composed of local linear perceptrons for approximating global class discriminants is investigated. Such local linear models may be combined in a cooperative or competitive way. In the cooperative model, a weighted sum of the outputs of the local perceptrons is computed where the weight is a function of the distance between the input and the position of the local perceptron. In the competitive model, the cost function dictates a mixture model where only one of the local perceptrons... (Update)
Context of citations to this paper: More
...the coupled approach is generally to be preferred. This has also been shown previously for the case of local linear models (Alpaydn and Jordan, 1996). Here we give equations to train a supervised mapping using gradient based learning. It is also possible to extend the...
.... ability of RBF can be extended by having the weight of each hidden unit, W jh , not a scalar but a linear function of the input [4]. This corresponds to a piecewise linear approximation of the discriminant instead of a piecewise constant approximation. 15 6...
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BibTeX entry: (Update)
Alpaydin, E. and Jordan, M. I. [1996], `Local linear perceptrons for classification', IEEE Transactions on Neural Networks 7(3), 788--792. http://citeseer.ist.psu.edu/alpaydin96local.html More
@article{ alpaydin96local,
author = "E. Alpaydin and Michael Jordan",
title = "Local Linear Perceptrons for Classification",
journal = "IEEE Transactions on Neural Networks",
volume = "7",
number = "3",
month = "May",
pages = "788--792",
year = "1996",
url = "citeseer.ist.psu.edu/alpaydin96local.html" }
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