Sciweavers

12 search results - page 1 / 3
» Iterative class discovery and feature selection using Minima...
Sort
View
BMCBI
2004
181views more  BMCBI 2004»
13 years 6 months ago
Iterative class discovery and feature selection using Minimal Spanning Trees
Background: Clustering is one of the most commonly used methods for discovering hidden structure in microarray gene expression data. Most current methods for clustering samples ar...
Sudhir Varma, Richard Simon
BMCBI
2010
178views more  BMCBI 2010»
13 years 6 months ago
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
Background: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example K...
David Edwards, Gabriel C. G. de Abreu, Rodrigo Lab...
IJCAI
1997
13 years 7 months ago
Minimum Splits Based Discretization for Continuous Features
Discretization refers to splitting the range of continuous values into intervals so as to provide useful information about classes. This is usually done by minimizing a goodness m...
Ke Wang, Han Chong Goh
BMCBI
2005
190views more  BMCBI 2005»
13 years 6 months ago
An Entropy-based gene selection method for cancer classification using microarray data
Background: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of n...
Xiaoxing Liu, Arun Krishnan, Adrian Mondry
ICASSP
2008
IEEE
14 years 24 days ago
Wavelet-domain compressive signal reconstruction using a Hidden Markov Tree model
Compressive sensing aims to recover a sparse or compressible signal from a small set of projections onto random vectors; conventional solutions involve linear programming or greed...
Marco F. Duarte, Michael B. Wakin, Richard G. Bara...