Showing papers on "Unsupervised learning published in 1984"
PARC1
TL;DR: It is determined that the higher the ratio of excitation to inhibition, the broader the equivalence class into which patterns are put together, and the self-organized automaton can display behavior that ranges from convergence into simple fixed points and oscillations to chaotic wanderings.
Abstract: We study the dynamical behavior of complex adaptive automata during unsupervised learning of periodic training sets A new technique for their analysis is presented and applied to an adaptive network with distributed memory We show that with general imput pattern sequences, the system can display behavior that ranges from convergence into simple fixed points and oscillations to chaotic wanderings We also test the ability of the self-organized automaton to recognize spatial patterns, discriminate between them, and to elicit meaningful information out of noisy inputs In this configuration we determine that the higher the ratio of excitation to inhibition, the broader the equivalence class into which patterns are put together
11 citations
TL;DR: This paper studies in theory, and gives a solution to the following concerns which may eventually be simultaneous: 1) obtain alternative classification decisions, ranked by some decreasing order of class membership probabilities; 2) imperfect teacher at the learning stage, or effects of labeling errors due to unsupervised learning by clustering; 3) noncooperative teacher, manipulating the a priori class probabilities.
Abstract: This paper studies in theory, and gives a solution to, the following concerns which may eventually be simultaneous: 1) obtain alternative classification decisions, ranked by some decreasing order of class membership probabilities; 2) imperfect teacher at the learning stage, or effects of labeling errors due to unsupervised learning by clustering; 3) noncooperative teacher, manipulating the a priori class probabilities; 4) unknown a priori class probabilities. These requirements are taken into account by considering a game between the recognition system and the teacher, in a game theoretical framework. Both players will ultimately select ``mixed strategies,'' which are probability distributions over the set of N alternative pattern classes, determined for each feature vector to be classified. This solution concept is interpreted in terms of the requirements 1)-4); numerical algorithms, as well as numerical examples are given with their solutions.
4 citations
TL;DR: This approach, and the concerns (1)–(4) are especially relevant for the performance enhancement of a number of acoustic signal classification systems (e.g., seismic exploration, intrusion detection, sonar).
2 citations
01 Jan 1984
TL;DR: It is determined that the higher the ratio of excitation to inhibition, the broader the equivalence class into which patterns are put together, and the self-organized automaton can display behavior that ranges from convergence into simple fixed points and oscillations to chaotic wanderings.
Abstract: We study the dynamical behavior of complex adaptive automata during unsupervised learning of periodic training sets. A new technique for their analysis is presented and applied to an adaptive network with distributed memory. We show that with general imput pattern sequences, the system can display behavior that ranges from convergence into simple fixed points and oscillations to chaotic wanderings. We also test the ability of the self-organized automaton to recognize spatial patterns, discriminate between them, and to elicit meaningful information out of noisy inputs. In this configuration we determine that the higher the ratio of excitation to inhibition, the broader the equivalence class into which patterns are put together.
2 citations