23 search results - page 3 / 5 » On Kernels, Margins, and Low-Dimensional Mappings |
96
Voted
FGR
2006
IEEE
15 years 4 months ago
2006
IEEE
Characteristics of the 2D shape deformation in human motion contain rich information for human identification and pose estimation. In this paper, we introduce a framework for sim...
82
Voted
CVPR
2008
IEEE
16 years 7 days ago
2008
IEEE
Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the...
ICML
2006
IEEE
15 years 11 months ago
2006
IEEE
Kernel functions have become an extremely popular tool in machine learning, with an attractive theory as well. This theory views a kernel as implicitly mapping data points into a ...
117
Voted
NIPS
2001
14 years 11 months ago
2001
Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why K-Neares...
91
Voted
CORR
2006
Springer
14 years 10 months ago
2006
Springer
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...