8 research outputs found
Approaches to working in high-dimensional data spaces: gene expression microarrays
This review provides a focused summary of the implications of high-dimensional data spaces produced by gene expression microarrays for building better models of cancer diagnosis, prognosis, and therapeutics. We identify the unique challenges posed by high dimensionality to highlight methodological problems and discuss recent methods in predictive classification, unsupervised subclass discovery, and marker identification
Text Document Classification Using Finite Mixtures.
Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi