750 search results - page 4 / 150 » Learning SVMs from Sloppily Labeled Data |
NIPS
1998
1998
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data
13 years 9 months ago
This paper presents probabilistic modeling methods to solve the problem of discriminating between five facial orientations with very little labeled data. Three models are explored...
ICANN
2001
Springer
2001
Springer
Learning and Prediction of the Nonlinear Dynamics of Biological Neurons with Support Vector Machines
14 years 10 days ago
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We...
BMCBI
2008
13 years 8 months ago
2008
Background: Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for S...
EMNLP
2009
13 years 5 months ago
2009
We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
SIGIR
2006
ACM
14 years 1 months ago
2006
ACM
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...