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Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems (1998)
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| Venue: | Proceedings of the IEEE |
| Citations: | 321 - 20 self |
BibTeX
@INPROCEEDINGS{Rose98deterministicannealing,
author = {Kenneth Rose},
title = {Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems},
booktitle = {Proceedings of the IEEE},
year = {1998},
pages = {2210--2239}
}
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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.
Keyphrases
related optimization problem neural network deterministic annealing similar optimization problem signal processing ill-defined boundary meeting ground information theory distant field unique position fertile soil probability theory engineering discipline complexity theory neural network perspective
