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

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Venue: | Proceedings of the IEEE |

Citations: | 247 - 11 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.