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» Active sampling for detecting irrelevant features
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ICML
2006
IEEE
10 years 11 months ago
Active sampling for detecting irrelevant features
The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the...
Sriharsha Veeramachaneni, Emanuele Olivetti, Paolo...
AI
2004
Springer
9 years 10 months ago
A selective sampling approach to active feature selection
Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and imp...
Huan Liu, Hiroshi Motoda, Lei Yu
IJCNN
2008
IEEE
10 years 5 months ago
Active Meta-Learning with Uncertainty Sampling and Outlier Detection
Abstract— Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this cont...
Ricardo Bastos Cavalcante Prudêncio, Teresa ...
BMCBI
2004
181views more  BMCBI 2004»
9 years 10 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
KI
2007
Springer
9 years 10 months ago
Improving the Detection of Unknown Computer Worms Activity Using Active Learning
Detecting unknown worms is a challenging task. Extant solutions, such as anti-virus tools, rely mainly on prior explicit knowledge of specific worm signatures. As a result, after t...
Robert Moskovitch, Nir Nissim, Dima Stopel, Clint ...
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