Skip Navigation

This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow FREE Full Text (Screen PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (2)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Chan, T. Y. T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Chan, T. Y. T.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics Vol. 17 no. 5 2001
Pages 438-444
© 2001 Oxford University Press

Unsupervised classification of noisy chromosomes

Tony Y. T. Chan

The University of Aizu, Aizu-Wakamatsu Shi, Fukushima Ken, 965-80 Japan

Received on May 17, 2000 ; accepted on December 22, 2000

Motivation: Almost all methods of chromosome recognition assume supervised training; i.e. we are given correctly classified chromosomes to start the training phase. Noise, if any, is confined only in the representation of the chromosomes and not in the classification of the chromosomes. During the recognition phase, the problem is simply to calculate the string edit distance of the unknowns to the representatives chosen from the training phase and classify the unknowns accordingly.

Results: In this paper, a general method to tackle the difficult unsupervised induction problem is described. The success of the method is demonstrated by showing how the inductive agent learns weights in a dynamic manner that allows it to distinguish between noisy median and telocentric chromosomes without knowing their proper labels. The process of learning is characterized as the process of finding the right distance function, i.e. the distance function that can nicely separate the classes.

Contact: t-chan{at}u-aizu.ac.jp


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Nucleic Acids ResHome page
H. Kurata, N. Matoba, and N. Shimizu
CADLIVE for constructing a large-scale biochemical network based on a simulation-directed notation and its application to yeast cell cycle
Nucleic Acids Res., July 15, 2003; 31(14): 4071 - 4084.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.