Showing papers on "Active learning (machine learning) published in 1973"
TL;DR: The results of computer experiments with artificially generated data and with handprinted alphanumeric characters are given to show that the approach proposed is quite useful for recognition of Markovian patterns.
5 citations
5 citations
01 Jan 1973
TL;DR: The model gives an overall view of the learning dynamics of an expanded range of training procedures, and provides insight into the training of multidimensional linear pattern classifiers, that represents discrete one-dimensional learning processes by a continuous two- dimensional learning process.
Abstract: We present a model of threshold learning that represents discrete one-dimensional learning processes by a continuous two-dimensional learning process. The model gives us an overall view of the learning dynamics of an expanded range of training procedures, and provides insight into the training of multidimensional linear pattern classifiers. In our model the expected performance is measured by learning curves, and the confidence in this expected performance is measured by variance curves. Previous work on the continuous approximation has been restricted to one-dimensional learning processes. The theory developed here promises to lead to improved training procedures and improved rules for determining when to stop training.
2 citations
TL;DR: Some mathematical models of the fourlayer machines are developed in which both excitory and inhibitory stimuli are developed and the performance of these models compare favourably with machines in which only excitory stimuli are used.
Abstract: In general, four-layer series-coupled machines can be divided into two types according to learning methods. One is the machine in which the change of variable connecting coefficients depends upon the state of association units in both layers A
I and A
II. The other type is the machine in which the change depends upon the state of association units in only layer A
I. In this paper, four-layer series-coupled machines of the latter type are discussed. They can be classified into six types according to the properties of the units and the learning algorithm. Some mathematical models of the machines are developed in which both excitory and inhibitory stimuli are used. The performance of these models compare favourably with machines in which only excitory stimuli are used. Learning procedure in each machine is analyzed and the convergence conditions are derived. Furthermore, some applications of the fourlayer machines to multi-category classification are discussed.
2 citations
01 Sep 1973
TL;DR: A new learning model is proposed, which is quite different from the usual ones in both function and structure, and the machine is shown to have the expected behavior.
Abstract: A new learning model is proposed, which is quite different from the usual ones in both function and structure. In the usual models only the classification function of input patterns is learned. On the other hand, the proposed learning machine can memorize input patterns themselves by a learning process. That is, an input pattern appears on the output plane of the machine if it has been presented to the machine often enough and does not appear if it has been presented infrequently. Since a man can remember or actually sketch patterns, he not only classifies but probably also memorizes patterns themselves by learning. This machine can therefore be said to simulate a phase of brain functioning. As for structure, the new machine consists of an iteration of nonlinear and spatially local processing instead of the usual single processing with linear discriminant functions. Both a general analysis and experimental results are given, and the machine is shown to have the expected behavior.