Population computation and majority inference in test tube

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We consider a probabilistic interpretation of the test tube which contains a large amount of DNA strands, and propose a population computation using a number of DNA strands in the test tube and a probabilistic logical inference based on the probabilistic interpretation. Second, in order for the DNA-based learning algorithm [4] to be robust for errors in the data, we implement the weighted majority algorithm [3] on DNA computers, called DNA-based majority algorithm via amplification (DNAMA), which take a strategy of “amplifying” the consistent (correct) DNA strands while the usual weighted majority algorithm decreases the weights of inconsistent ones. We show a theoretical analysis for the mistake bound of the DNA-based majority algorithm via amplification, and imply that the amplification to “double the volumes” of the correct DNA strands in the test tube works well.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages82-91
Number of pages10
Volume2340
ISBN (Print)3540437754
Publication statusPublished - 2002
Externally publishedYes
Event7th International Workshop on DNA-Based Computers, DNA 2001 - Tampa, United States
Duration: 2001 Jun 102001 Jun 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2340
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th International Workshop on DNA-Based Computers, DNA 2001
CountryUnited States
CityTampa
Period01/6/1001/6/13

Fingerprint

Tube
DNA
Amplification
Inconsistent
Learning Algorithm
Theoretical Analysis
Imply
Decrease
Learning algorithms
Interpretation

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Sakakibara, Y. (2002). Population computation and majority inference in test tube. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2340, pp. 82-91). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2340). Springer Verlag.

Population computation and majority inference in test tube. / Sakakibara, Yasubumi.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2340 Springer Verlag, 2002. p. 82-91 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2340).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sakakibara, Y 2002, Population computation and majority inference in test tube. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2340, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2340, Springer Verlag, pp. 82-91, 7th International Workshop on DNA-Based Computers, DNA 2001, Tampa, United States, 01/6/10.
Sakakibara Y. Population computation and majority inference in test tube. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2340. Springer Verlag. 2002. p. 82-91. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Sakakibara, Yasubumi. / Population computation and majority inference in test tube. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2340 Springer Verlag, 2002. pp. 82-91 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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