Sciweavers

304 search results - page 28 / 61
» How good are support vector machines
Sort
View
JMLR
2006
116views more  JMLR 2006»
13 years 7 months ago
Step Size Adaptation in Reproducing Kernel Hilbert Space
This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SMD) algorithm to adapt its step size automatically. We formulate the online lear...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex ...
ICIP
2001
IEEE
14 years 9 months ago
Face detection using large margin classifiers
Large margin classifiers have demonstrated their advantages in many visual learning tasks, and have attracted much attention in vision and image processing communities. In this pa...
Ming-Hsuan Yang, Dan Roth, Narendra Ahuja
SMC
2007
IEEE
113views Control Systems» more  SMC 2007»
14 years 2 months ago
Robust multi-modal biometric fusion via multiple SVMs
—Existing learning-based multi-modal biometric fusion techniques typically employ a single static Support Vector Machine (SVM). This type of fusion improves the accuracy of biome...
Sabra Dinerstein, Jonathan Dinerstein, Dan Ventura
AE
2007
Springer
14 years 1 months ago
A Study of Crossover Operators for Gene Selection of Microarray Data
Classification of microarray data requires the selection of a subset of relevant genes in order to achieve good classification performance. Several genetic algorithms have been d...
Jose Crispin Hernandez Hernandez, Béatrice ...
IJCNN
2006
IEEE
14 years 1 months ago
Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs
Abstract— While the model parameters of many kernel learning methods are given by the solution of a convex optimisation problem, the selection of good values for the kernel and r...
Gavin C. Cawley