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» Making inferences with small numbers of training sets
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CLADE
2008
IEEE
14 years 3 months ago
SWARM: a scientific workflow for supporting bayesian approaches to improve metabolic models
With the exponential growth of complete genome sequences, the analysis of these sequences is becoming a powerful approach to build genome-scale metabolic models. These models can ...
Xinghua Shi, Rick Stevens
ISMIR
2003
Springer
133views Music» more  ISMIR 2003»
14 years 2 months ago
Chord segmentation and recognition using EM-trained hidden markov models
Automatic extraction of content description from commercial audio recordings has a number of important applications, from indexing and retrieval through to novel musicological ana...
Alexander Sheh, Daniel P. W. Ellis
TMI
2010
160views more  TMI 2010»
13 years 7 months ago
Computer-Aided Detection of Polyps in CT Colonography Using Logistic Regression
—We present a computer-aided detection (CAD) system for computed tomography colonography that orders the polyps according to clinical relevance. The CAD system consists of two st...
Vincent Frans van Ravesteijn, Cees van Wijk, Frans...
BMCBI
2006
115views more  BMCBI 2006»
13 years 9 months ago
The accuracy of several multiple sequence alignment programs for proteins
Background: There have been many algorithms and software programs implemented for the inference of multiple sequence alignments of protein and DNA sequences. The "true" ...
Paulo A. S. Nuin, Zhouzhi Wang, Elisabeth R. M. Ti...
ICML
2007
IEEE
14 years 9 months ago
Maximum margin clustering made practical
Maximum margin clustering (MMC) is a recent large margin unsupervised learning approach that has often outperformed conventional clustering methods. Computationally, it involves n...
Kai Zhang, Ivor W. Tsang, James T. Kwok