Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems

Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems (1998)

by Kenneth Rose

Venue:	Proceedings of the IEEE
Citations:	193 - 4 self

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Datum	Value	Source
TITLE	Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems	user correction - Legacy Corrections
AUTHOR NAME	Kenneth Rose	SVM HeaderParse 0.1
AUTHOR AFFIL	; Invited Paper; Inc	SVM HeaderParse 0.2
ABSTRACT	this paper. Let us place it within the neural network perspective, and particularly that of learning. The area of neural networks has greatly benefited from its unique position at the crossroads of several diverse scientific and engineering disciplines including statistics and probability theory, physics, biology, control and signal processing, information theory, complexity theory, and psychology (see [45]). Neural networks have provided a fertile soil for the infusion (and occasionally confusion) of ideas, as well as a meeting ground for comparing viewpoints, sharing tools, and renovating approaches. It is within the ill-defined boundaries of the field of neural networks that researchers in traditionally distant fields have come to the realization that they have been attacking fundamentally similar optimization problems.	user correction - Legacy Corrections
YEAR	1998	INFERENCE
VENUE	Proceedings of the IEEE	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	2210--2239	INFERENCE
VOLUME	86	INFERENCE
CITATIONS	110 found	ParsCit 1.0