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» Optimal feature selection for support vector machines
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BMCBI
2008
165views more  BMCBI 2008»
13 years 7 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...
ICMI
2003
Springer
184views Biometrics» more  ICMI 2003»
14 years 27 days ago
Real time facial expression recognition in video using support vector machines
Enabling computer systems to recognize facial expressions and infer emotions from them in real time presents a challenging research topic. In this paper, we present a real time ap...
Philipp Michel, Rana El Kaliouby
ICPR
2004
IEEE
14 years 8 months ago
Support Vector Machine with Local Summation Kernel for Robust Face Recognition
This paper presents Support Vector Machine (SVM) with local summation kernel for robust face recognition. In recent years, the effectiveness of SVM and local features is reported....
Kazuhiro Hotta
ICCV
2007
IEEE
14 years 2 months ago
Co-Tracking Using Semi-Supervised Support Vector Machines
This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...
Feng Tang, Shane Brennan, Qi Zhao, Hai Tao
ECAI
2004
Springer
14 years 1 months ago
A Generalized Quadratic Loss for Support Vector Machines
The standard SVM formulation for binary classification is based on the Hinge loss function, where errors are considered not correlated. Due to this, local information in the featu...
Filippo Portera, Alessandro Sperduti