Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Convergence of alternating optimization
Neural, Parallel & Scientific Computations
A regularization framework for multiple-instance learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
On the relation between multi-instance learning and semi-supervised learning
Proceedings of the 24th international conference on Machine learning
A Convex Method for Locating Regions of Interest with Multi-instance Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Optimizing cepstral features for audio classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We propose a direct approach to learning sparse Support Vector Machine (SVM) prediction models for Multi-Instance (MI) classification. The proposed sparse SVM is based on a "label-mean" formulation of MI classification which takes the average of predictions of individual instances for bag-level prediction. This leads to a convex optimization problem, which is essential for the tractability of the optimization problem arising from the sparse SVM formulation we derived subsequently, as well as the validity of the optimization strategy we employed to solve it. Based on the "label-mean" formulation, we can build sparse SVM models for MI classification and explicitly control their sparsities by enforcing the maximum number of expansions allowed in the prediction function. An effective optimization strategy is adopted to solve the formulated sparse learning problem which involves the learning of both the classifier and the expansion vectors. Experimental results on benchmark data sets have demonstrated that the proposed approach is effective in building very sparse SVM models while achieving comparable performance to the state-of-the-art MI classifiers.