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On Learning µ-Perceptron Networks On the Uniform Distribution (1995)
| Content Provider | CiteSeerX |
|---|---|
| Author | Golea, Mostefa Marchand, Mario Hancock, Thomas R. |
| Abstract | We investigate the learnability, under the uniform distribution, of neural concepts that can be represented as simple combinations of nonoverlapping perceptrons (also called perceptrons) with binary weights and arbitrary thresholds. Two perceptrons are said to be nonoverlapping if they do not share any input variables. Specifically, we investigate, within the distribution-specific PAC model, the learnability of perceptron unions, decision lists , and generalized decision lists . In contrast to most neural network learning algorithms, we do not assume that the architecture of the network is known in advance. Rather, it is the task of the algorithm to find both the architecture of the net and the weight values necessary to represent the function to be learned. We give polynomial time algorithms for learning these restricted classes of networks. The algorithms work by estimating various statistical quantities that yield enough information to infer, with high probability, the target con... |
| File Format | |
| Volume Number | 9 |
| Journal | NEURAL NETWORKS |
| Language | English |
| Publisher Date | 1995-01-01 |
| Access Restriction | Open |
| Subject Keyword | Uniform Distribution Learning Perceptron Network Arbitrary Threshold Simple Combination Input Variable Polynomial Time Algorithm Binary Weight Generalized Decision List Weight Value Algorithm Work Decision List Neural Concept Target Con Various Statistical Quantity Enough Information High Probability Distribution-specific Pac Model Neural Network Perceptron Union |
| Content Type | Text |
| Resource Type | Article |
