54 search results - page 2 / 11 » Semi-supervised learning by locally linear embedding in kern... |
ICIP
2005
IEEE
14 years 6 months ago
2005
IEEE
In this paper, we propose a novel classification method, called local manifold matching (LMM), for face recognition. LMM has great representational capacity of available prototypes...
CIBCB
2005
IEEE
13 years 10 months ago
2005
IEEE
— Part of the challenge of modeling protein sequences is their discrete nature. Many of the most powerful statistical and learning techniques are applicable to points in a Euclid...
CVPR
2010
IEEE
13 years 7 months ago
2010
IEEE
Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel ...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and disc...
NIPS
2008
13 years 6 months ago
2008
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...