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Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Probabilistic latent semantic indexing
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Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Object Recognition from Local Scale-Invariant Features
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Minimizing Nonconvex Nonsmooth Functions via Cutting Planes and Proximity Control
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Kernel Methods for Pattern Analysis
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Image Categorization by Learning and Reasoning with Regions
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MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Training linear SVMs in linear time
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Linear prediction models with graph regularization for web-page categorization
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MILES: Multiple-Instance Learning via Embedded Instance Selection
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Multiple instance learning for sparse positive bags
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Introduction to Information Retrieval
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Localized Content-Based Image Retrieval
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M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Marginalized multi-instance kernels
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
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Bundle Methods for Regularized Risk Minimization
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Pegasos: primal estimated sub-gradient solver for SVM
Mathematical Programming: Series A and B - Special Issue on "Optimization and Machine learning"; Alexandre d’Aspremont • Francis Bach • Inderjit S. Dhillon • Bin Yu
Robust Object Tracking with Online Multiple Instance Learning
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Multi-view transfer learning with a large margin approach
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In Multiple Instance Learning (MIL), each entity is normally expressed as a set of instances. Most of the current MIL methods only deal with the case when each instance is represented by one type of features. However, in many real world applications, entities are often described from several different information sources/views. For example, when applying MIL to image categorization, the characteristics of each image can be derived from both its RGB features and SIFT features. Previous research work has shown that, in traditional learning methods, leveraging the consistencies between different information sources could improve the classification performance drastically. Out of a similar motivation, to incorporate the consistencies between different information sources into MIL, we propose a novel research framework -- Multi-Instance Learning from Multiple Information Sources (MI2LS). Based on this framework, an algorithm -- Fast MI2LS (FMI2LS) is designed, which combines Concave-Convex Constraint Programming (CCCP) method and an adapte- d Stoachastic Gradient Descent (SGD) method. Some theoretical analysis on the optimality of the adapted SGD method and the generalized error bound of the formulation are given based on the proposed method. Experimental results on document classification and a novel application -- Insider Threat Detection (ITD), clearly demonstrate the superior performance of the proposed method over state-of-the-art MIL methods.