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CIBCB
2007
IEEE
9 years 3 months ago
A Comparison of Sequence Kernels for Localization Prediction of Transmembrane Proteins
Abstract— We applied Support Vector Machines to the prediction of the subcellular localization of transmembrane proteins, and compared the performance of different sequence kerne...
Stefan Maetschke, Marcus Gallagher, Mikael Bod&eac...
IJCNN
2008
IEEE
9 years 3 months ago
Sparse support vector machines trained in the reduced empirical feature space
— We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky...
Kazuki Iwamura, Shigeo Abe
ICPR
2008
IEEE
9 years 3 months ago
Pre-extracting method for SVM classification based on the non-parametric K-NN rule
With the increase of the training set’s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel preextracting method f...
Deqiang Han, Chongzhao Han, Yi Yang, Yu Liu, Wenta...
DCC
2009
IEEE
9 years 9 months ago
Compressed Kernel Perceptrons
Kernel machines are a popular class of machine learning algorithms that achieve state of the art accuracies on many real-life classification problems. Kernel perceptrons are among...
Slobodan Vucetic, Vladimir Coric, Zhuang Wang
ICML
2005
IEEE
9 years 10 months ago
The cross entropy method for classification
We consider support vector machines for binary classification. As opposed to most approaches we use the number of support vectors (the "L0 norm") as a regularizing term ...
Shie Mannor, Dori Peleg, Reuven Y. Rubinstein
ICML
2009
IEEE
9 years 10 months ago
A simpler unified analysis of budget perceptrons
The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing ...
Ilya Sutskever
ICPR
2006
IEEE
9 years 10 months ago
Metric tree partitioning and Taylor approximation for fast support vector classification
This paper presents a method to speed up support vector classification, especially important when data is highdimensional. Unlike previous approaches which focus on less support v...
Thang V. Pham, Arnold W. M. Smeulders
ICIP
2002
IEEE
9 years 11 months ago
Face detection using coarse-to-fine support vector classifiers
We describe a new face detection algorithm based on a hierarchy of support vector classifiers (SVMs) designed for efficient computation. The hierarchy serves as a platform for a c...
Hichem Sahbi, Donald Geman, Nozha Boujemaa
CVPR
2003
IEEE
9 years 11 months ago
Subset Selection for Efficient SVM Tracking
We update the SVM score of an object through a video sequence with a small and variable subset of support vectors. In the first frame we use all the support vectors to compute the...
Shai Avidan
CVPR
2001
IEEE
9 years 11 months ago
Support Vector Tracking
Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function betwee...
Shai Avidan
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