A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Linear Programming Boosting via Column Generation
Machine Learning
Combining Multiple Representations and Classifiers for Pen-based Handwritten Digit Recognitio
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
An introduction to variable and feature selection
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Artificial Intelligence in Medicine
Harmonium Models for Video Classification
Statistical Analysis and Data Mining
Expression recognition in videos using a weighted component-based feature descriptor
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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Multiple kernel learning (MKL) approach for selecting and combining different representations of a data is presented. Selection of features from a representation of data using the MKL approach is also addressed. A base kernel function is used for each representation as well as for each feature from a representation. A new kernel is obtained as a linear combination of base kernels, weighted according to the relevance of representation or feature. The MKL approach helps to select and combine the representations as well as to select features from a representation. Issues in the MKL algorithm are addressed in the framework of support vector machines (SVM). Studies on the representation and feature selection are presented for an image categorization task.