The Library
Evolutionary trees can be learned in polynomial time in the two-state general Markov model
Tools
Tools
Tools
Cryan, Mary, Goldberg, Leslie Ann and Goldberg, Paul W. (1998) Evolutionary trees can be learned in polynomial time in the two-state general Markov model. University of Warwick. Department of Computer Science. (Department of Computer Science research report). (Unpublished)
|
PDF (Department of Computer Science Research Report)
WRAP_cs-rr-347.pdf - Other - Requires a PDF viewer. Download (426Kb) | Preview |
Abstract
The j-State General Markov Model of evolution (due to Steel) is a stochastic model concerned with the evolution of strings over an alphabet of size j. In particular, the Two-State General Markov Model of evolution generalises the well-known Cavender-Farris-Neyman model of evolution by removing the symmetry restriction (which requires that the probability that a '0' turns into a '1' along an edge is the same as the probability that a '1' turns into a '0' along the edge). Farach and Kannan showed how to PAC-learn Markov Evolutionary Trees in the Cavender-Farris-Neyman model provided that the target tree satisfies the additional restriction that all pairs of leaves have a sufficiently high probability of being the same. We show how to remove both restrictions and thereby obtain the first polynomial-time PAC-learning algorithm (in the sense of Kearns et al.) for the general class of Two-State Markov Evolutionary Trees.
| Item Type: | Report | ||||
|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||
| Divisions: | Faculty of Science > Computer Science | ||||
| Library of Congress Subject Headings (LCSH): | Markov processes, Graph theory | ||||
| Series Name: | Department of Computer Science research report | ||||
| Publisher: | University of Warwick. Department of Computer Science | ||||
| Official Date: | 20 July 1998 | ||||
| Dates: |
|
||||
| Number: | Number 347 | ||||
| Number of Pages: | 21 | ||||
| DOI: | CS-RR-347 | ||||
| Institution: | University of Warwick | ||||
| Theses Department: | Department of Computer Science | ||||
| Status: | Not Peer Reviewed | ||||
| Publication Status: | Unpublished | ||||
| Funder: | European Strategic Programme of Research and Development in Information Technology (ESPRIT), Engineering and Physical Sciences Research Council (EPSRC) | ||||
| Grant number: | 20244 (ESPRIT), 21726 (ESPRIT), GR/L60982 (EPSRC) | ||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
