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NIPS
2008
13 years 10 months ago
Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning
For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depe...
Francis Bach
IJCV
2008
106views more  IJCV 2008»
13 years 9 months ago
A Model-Selection Framework for Multibody Structure-and-Motion of Image Sequences
Given an image sequence of a scene consisting of multiple rigidly moving objects, multi-body structure-and-motion (MSaM) is the task to segment the image feature tracks into the d...
Konrad Schindler, David Suter, Hanzi Wang
ICCV
2007
IEEE
14 years 11 months ago
Vector Quantizing Feature Space with a Regular Lattice
Most recent class-level object recognition systems work with visual words, i.e., vector quantized local descriptors. In this paper we examine the feasibility of a dataindependent ...
Tinne Tuytelaars, Cordelia Schmid
PR
2008
93views more  PR 2008»
13 years 9 months ago
Genetic algorithm-based feature set partitioning for classification problems
Feature set partitioning generalizes the task of feature selection by partitioning the feature set into subsets of features that are collectively useful, rather than by finding a ...
Lior Rokach
MICCAI
2005
Springer
14 years 2 months ago
Learning Best Features for Deformable Registration of MR Brains
Abstract. This paper presents a learning method to select best geometric features for deformable brain registration. Best geometric features are selected for each brain location, a...
Guorong Wu, Feihu Qi, Dinggang Shen