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» Support Vector Regression Using Mahalanobis Kernels
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MCS
2005
Springer
14 years 2 months ago
Ensemble of SVMs for Incremental Learning
Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems. However, SVMs suffer from the catastrophic forgetti...
Zeki Erdem, Robi Polikar, Fikret S. Gürgen, N...
BMCBI
2008
158views more  BMCBI 2008»
13 years 9 months ago
Real value prediction of protein solvent accessibility using enhanced PSSM features
Background: Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from...
Darby Tien-Hao Chang, Hsuan-Yu Huang, Yu-Tang Syu,...
KDD
2007
ACM
132views Data Mining» more  KDD 2007»
14 years 9 months ago
A scalable modular convex solver for regularized risk minimization
A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different r...
Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanatha...
BMVC
2000
13 years 10 months ago
Recognising the Dynamics of Faces across Multiple Views
We present an integrated framework for dynamic face detection and recognition, where head pose is estimated using Support Vector Regression, face detection is performed by Support...
Yongmin Li, Shaogang Gong, Heather M. Liddell
CN
2010
110views more  CN 2010»
13 years 9 months ago
End-to-end quality of service seen by applications: A statistical learning approach
The focus of this work is on the estimation of quality of service (QoS) parameters seen by an application. Our proposal is based on end-to-end active measurements and statistical ...
Pablo Belzarena, Laura Aspirot