A "Thermal" Perceptron Learning Rule

A "Thermal" Perceptron Learning Rule

The thermal perceptron is a simple extension to Rosenblatt’s perceptron learning rule for training individual linear threshold units. It finds stable weights for nonseparable problems as well as separable ones. Experiments indicate that if a good initial setting for a temperature parameter, To, has been found, then the thermal perceptron outperforms the Pocket algorithm and methods based on gra...

Minimerror: a perceptron learning rule that finds the optimal weights

Information theory approach to learning of the perceptron rule.

By recourse to a method based on information theory, we have studied the generalization problem in perceptrons. We considered different a priori distributions about the weights of the teacher perceptron. Our approach allows us to define the information gain from the examples used in the training procedure. The information gain can be used to choose a convenient example set for training the perc...

Multi-state perceptrons: learning rule and perceptron of maximal stability

A new perceptron learning rule which works with multilayer neural networks made of multi-state units is obtained, and the corresponding convergence theorem is proved. The deenition of perceptron of maximal stability is enlarged in order to include these new multi-state perceptrons, and a proof of existence and uniqueness of such optimal solutions is outlined.

Statistical Mechanics of On-line Ensemble Teacher Learning through a Novel Perceptron Learning Rule

In ensemble teacher learning, ensemble teachers have only uncertain information about the true teacher, and this information is given by an ensemble consisting of an infinite number of ensemble teachers whose variety is sufficiently rich. In this learning, a student learns from an ensemble teacher that is iteratively selected randomly from a pool of many ensemble teachers. An interesting point ...