750 search results - page 48 / 150 » Learning SVMs from Sloppily Labeled Data |
KDD
2008
ACM
14 years 9 months ago
2008
ACM
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world ap...
KDD
2010
ACM
13 years 11 months ago
2010
ACM
Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe ...
ESWS
2010
Springer
13 years 11 months ago
2010
Springer
In the Linked Open Data cloud one of the largest data sets, comprising of 2.5 billion triples, is derived from the Life Science domain. Yet this represents a small fraction of the ...
AAAI
2011
12 years 9 months ago
2011
A common approach in machine learning is to use a large amount of labeled data to train a model. Usually this model can then only be used to classify data in the same feature spac...
ICDM
2003
IEEE
14 years 2 months ago
2003
IEEE
Predictive data mining typically relies on labeled data without exploiting a much larger amount of available unlabeled data. The goal of this paper is to show that using unlabeled...