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» Using Model Counting to Find Optimal Distinguishing Tests
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TIT
2008
102views more  TIT 2008»
14 years 9 months ago
On Low-Complexity Maximum-Likelihood Decoding of Convolutional Codes
Abstract--This letter considers the average complexity of maximum-likelihood (ML) decoding of convolutional codes. ML decoding can be modeled as finding the most probable path take...
Jie Luo
81
Voted
BMCBI
2006
165views more  BMCBI 2006»
14 years 9 months ago
Improving the quality of protein structure models by selecting from alignment alternatives
Background: In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish hig...
Ingolf Sommer, Stefano Toppo, Oliver Sander, Thoma...
KDD
2008
ACM
183views Data Mining» more  KDD 2008»
15 years 10 months ago
Knowledge transfer via multiple model local structure mapping
The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from wh...
Jing Gao, Wei Fan, Jing Jiang, Jiawei Han
BMCBI
2007
190views more  BMCBI 2007»
14 years 9 months ago
Discriminative motif discovery in DNA and protein sequences using the DEME algorithm
Background: Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to i...
Emma Redhead, Timothy L. Bailey
103
Voted
CSL
2010
Springer
14 years 9 months ago
Monaural speech separation and recognition challenge
Robust speech recognition in everyday conditions requires the solution to a number of challenging problems, not least the ability to handle multiple sound sources. The specific ca...
Martin Cooke, John R. Hershey, Steven J. Rennie