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ICML
2004
IEEE
16 years 3 months ago
Multiple kernel learning, conic duality, and the SMO algorithm
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. ...
Francis R. Bach, Gert R. G. Lanckriet, Michael I. ...
ICPR
2008
IEEE
15 years 8 months ago
Learning visual dictionaries and decision lists for object recognition
Visual dictionaries are widely employed in object recognition to map unordered bags of local region descriptors into feature vectors for image classification. Most visual dictiona...
Wei Zhang, Thomas G. Dietterich
AUSAI
2008
Springer
15 years 4 months ago
Learning to Find Relevant Biological Articles without Negative Training Examples
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
Keith Noto, Milton H. Saier Jr., Charles Elkan
CORR
2000
Springer
126views Education» more  CORR 2000»
15 years 2 months ago
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
We investigate the performance of two machine learning algorithms in the context of antispam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a nee...
Ion Androutsopoulos, Georgios Paliouras, Vangelis ...
BICOB
2010
Springer
15 years 8 days ago
Multiple Kernel Learning for Fold Recognition
Fold recognition is a key problem in computational biology that involves classifying protein sharing structural similarities into classes commonly known as "folds". Rece...
Huzefa Rangwala