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New algorithms for special cases of the hidden line elimination problem

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R H Güting,

Th. OttmanAuthors Info & Claims

Proceedings on STACS 85 2nd annual symposium on theoretical aspects of computer science

Pages 161 - 172

Published: 01 December 1985 Publication History

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Goodrich MGhouse MBright JLeighton F(1990)Generalized sweep methods for parallel computational geometryProceedings of the second annual ACM symposium on Parallel algorithms and architectures10.1145/97444.97695(280-289)Online publication date: 1-May-1990
https://dl.acm.org/doi/10.1145/97444.97695
Bern MEdelsbrunner H(1988)Hidden surface removal for rectanglesProceedings of the fourth annual symposium on Computational geometry10.1145/73393.73412(183-192)Online publication date: 6-Jan-1988
https://dl.acm.org/doi/10.1145/73393.73412

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New algorithms for special cases of the hidden line elimination problem

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New sequence processing algorithms using hidden markov models
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Smooth on-line learning algorithms for hidden Markov models

A simple learning algorithm for Hidden Markov Models (HMMs) is presented together with a number of variations. Unlike other classical algorithms such as the Baum-Welch algorithm, the algorithms described are smooth and can be used on-line (after each ...

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Proceedings on STACS 85 2nd annual symposium on theoretical aspects of computer science

December 1985

374 pages

ISBN:0387139125

Editor:
K Mehlhorn

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Springer-Verlag

Berlin, Heidelberg

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Published: 01 December 1985

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Goodrich MGhouse MBright JLeighton F(1990)Generalized sweep methods for parallel computational geometryProceedings of the second annual ACM symposium on Parallel algorithms and architectures10.1145/97444.97695(280-289)Online publication date: 1-May-1990
https://dl.acm.org/doi/10.1145/97444.97695
Bern MEdelsbrunner H(1988)Hidden surface removal for rectanglesProceedings of the fourth annual symposium on Computational geometry10.1145/73393.73412(183-192)Online publication date: 6-Jan-1988
https://dl.acm.org/doi/10.1145/73393.73412

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New sequence processing algorithms using hidden markov models

New algorithms for computing groebner bases

Smooth on-line learning algorithms for hidden Markov models

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