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MICCAI
2002
Springer
14 years 8 months ago
Comparative Exudate Classification Using Support Vector Machines and Neural Networks
After segmenting candidate exudates regions in colour retinal images we present and compare two methods for their classification. The Neural Network based approach performs margina...
Alireza Osareh, Majid Mirmehdi, Barry T. Thomas, R...
JMLR
2008
123views more  JMLR 2008»
13 years 7 months ago
Optimization Techniques for Semi-Supervised Support Vector Machines
Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs...
Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keer...
NIPS
2008
13 years 9 months ago
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
This work characterizes the generalization ability of algorithms whose predictions are linear in the input vector. To this end, we provide sharp bounds for Rademacher and Gaussian...
Sham M. Kakade, Karthik Sridharan, Ambuj Tewari
IBPRIA
2005
Springer
14 years 29 days ago
Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning
A natural way to deal with training samples in imbalanced class problems is to prune them removing redundant patterns, easy to classify and probably over represented, and label noi...
Iván Cantador, José R. Dorronsoro
ICML
2007
IEEE
14 years 8 months ago
Solving multiclass support vector machines with LaRank
Optimization algorithms for large margin multiclass recognizers are often too costly to handle ambitious problems with structured outputs and exponential numbers of classes. Optim...
Antoine Bordes, Jason Weston, Léon Bottou, ...