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- Alan B. Tickle, Robert Andrews, Mostefa Golea, Joachim Diederich
- IEEE Trans. Neural Networks
- 1998

To date, the preponderance of techniques for eliciting the knowledge embedded in trained artificial neural networks (ANN's) has focused primarily on extracting rule-based explanations from… (More)

We consider a perceptron with Ni input units, one output and a yet unspecified number of hidden units. This perceptron must be able to learn a given but arbitrary set of input-output examples. By… (More)

In this paper, we We a close look at the problem of l&ing simple neural concepts under the uniform diseibution of examples By simple neural concepts we mean concepts that can be represented as simple… (More)

- Thomas R. Hancock, Mostefa Golea, Mario Marchand
- Machine Learning
- 1994

We investigate, within the PAC learning model, the problem of learning nonoverlapping perceptron networks (also known as read-once formulas over a weighted threshold basis). These are loop-free… (More)

- Mostefa Golea
- 1996

- Mostefa Golea, Peter L. Bartlett, Wee Sun Lee, Llew Mason
- NIPS
- 1997

Recent theoretical results for pattern classification with thresholded real-valued functions (such as support vector machines, sigmoid networks, and boosting) give bounds on misclassification… (More)

- Mostefa Golea, Mario Marchand
- Neural Computation
- 1993

We present an algorithm that PAC learns any perceptron with binary weights and arbitrary threshold under the family of product distributions. The sample complexity of this algorithm is of O[(n/)4… (More)

This paper explores the application of neural network principles to the construction of decision trees from examples. We consider the problem of constructing a tree of perceptrons able to execute a… (More)

- Mostefa Golea, Mario Marchand, Thomas R. Hancock
- NIPS
- 1992

Neural networks with binary weights are very important from both the theoretical and practical points of view. In this paper, we investigate the learnability of single binary perceptrons and unions… (More)

- Mostefa Golea, Mario Marchand, Thomas R. Hancock
- Neural Networks
- 1996

We investigate the learnability, under the uniform distribution, of neural concepts that can be represented as simple combinations of nonoverlapping perceptrons (also called μ-perceptrons) with… (More)