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» Robust feature induction for support vector machines
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PKDD
2009
Springer
88views Data Mining» more  PKDD 2009»
15 years 4 months ago
Feature Weighting Using Margin and Radius Based Error Bound Optimization in SVMs
The Support Vector Machine error bound is a function of the margin and radius. Standard SVM algorithms maximize the margin within a given feature space, therefore the radius is fi...
Huyen Do, Alexandros Kalousis, Melanie Hilario
BMCBI
2008
165views more  BMCBI 2008»
14 years 10 months ago
Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics
Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides...
Wiebke Timm, Alexandra Scherbart, Sebastian Bö...
WCE
2007
14 years 11 months ago
Gene Selection for Tumor Classification Using Microarray Gene Expression Data
– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...
KDD
2004
ACM
330views Data Mining» more  KDD 2004»
15 years 10 months ago
Learning to detect malicious executables in the wild
In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and enc...
Jeremy Z. Kolter, Marcus A. Maloof
SCHOLARPEDIA
2008
89views more  SCHOLARPEDIA 2008»
14 years 8 months ago
Support vector clustering
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to d...
Asa Ben-Hur