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» Learning with Rigorous Support Vector Machines
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KDD
2012
ACM
205views Data Mining» more  KDD 2012»
13 years 2 months ago
Rank-loss support instance machines for MIML instance annotation
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
Forrest Briggs, Xiaoli Z. Fern, Raviv Raich
ICMLA
2010
14 years 9 months ago
Using Randomised Vectors in Transcription Factor Binding Site Predictions
Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may ...
Faisal Rezwan, Yi Sun, Neil Davey, Rod Adams, Alis...
JMLR
2006
124views more  JMLR 2006»
14 years 11 months ago
A Direct Method for Building Sparse Kernel Learning Algorithms
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
Mingrui Wu, Bernhard Schölkopf, Gökhan H...
ICML
2010
IEEE
15 years 28 days ago
SVM Classifier Estimation from Group Probabilities
A learning problem that has only recently gained attention in the machine learning community is that of learning a classifier from group probabilities. It is a learning task that ...
Stefan Rüping
ICML
2003
IEEE
16 years 20 days ago
Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers
We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-W...
Aldebaro Klautau, Nikola Jevtic, Alon Orlitsky