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ALGORITHMICA
2006
74views more  ALGORITHMICA 2006»
15 years 4 months ago
Parallelizing Feature Selection
Classification is a key problem in machine learning/data mining. Algorithms for classification have the ability to predict the class of a new instance after having been trained on...
Jerffeson Teixeira de Souza, Stan Matwin, Nathalie...
ICML
2004
IEEE
16 years 5 months ago
Margin based feature selection - theory and algorithms
Feature selection is the task of choosing a small set out of a given set of features that capture the relevant properties of the data. In the context of supervised classification ...
Ran Gilad-Bachrach, Amir Navot, Naftali Tishby
ICML
2004
IEEE
16 years 5 months ago
Robust feature induction for support vector machines
The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, no...
Rong Jin, Huan Liu
SSIAI
2002
IEEE
15 years 9 months ago
Entropy Estimation for Segmentation of Multi-Spectral Chromosome Images
In the early 1990s, the state-of-the-art in commercial chromosome image acquisition was grayscale. Automated chromosome classification was based on the grayscale image and boundar...
Wade Schwartzkopf, Brian L. Evans, Alan C. Bovik
TNN
2008
132views more  TNN 2008»
15 years 4 months ago
Just-in-Time Adaptive Classifiers - Part I: Detecting Nonstationary Changes
Abstract--The stationarity requirement for the process generating the data is a common assumption in classifiers' design. When such hypothesis does not hold, e.g., in applicat...
Cesare Alippi, Manuel Roveri