Multiple instance learning via margin maximization

Authors:
O. Erhun Kundakcioglu;Onur Seref;Panos M. Pardalos
Affiliations:
Department of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, PO Box 116595, Gainesville, FL 32611, USA;Department of Business Information Technology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA;Department of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, PO Box 116595, Gainesville, FL 32611, USA and Center for Applied Optimization, University of Florida, 401 We ...
Venue:
Applied Numerical Mathematics
Year:
2010

Citing 14
Cited 1

Solving the multiple instance problem with axis-parallel rectangles

Artificial Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Solving the Multiple-Instance Problem: A Lazy Learning Approach

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Multiple instance learning with generalized support vector machines

Eighteenth national conference on Artificial intelligence
Multiple instance learning of real valued data

The Journal of Machine Learning Research
Image Categorization by Learning and Reasoning with Regions

The Journal of Machine Learning Research
Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application

The Journal of Machine Learning Research
MILES: Multiple-Instance Learning via Embedded Instance Selection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Incorporating multiple SVMs for automatic image annotation

Pattern Recognition
Supervised Learning of Semantic Classes for Image Annotation and Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian multiple instance learning: automatic feature selection and inductive transfer

Proceedings of the 25th international conference on Machine learning
Supervised Learning by Training on Aggregate Outputs

ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Neural network based image retrieval with multiple instance leaning techniques

KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II

On multiple-instance learning of halfspaces

Information Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we consider the classification problem within the multiple instance learning (MIL) context. Training data is composed of labeled bags of instances. Despite the large number of margin maximization based classification methods, there are only a few methods that consider the margin for MIL problems in the literature. We first formulate a combinatorial margin maximization problem for multiple instance classification and prove that it is NP-hard. We present a way to apply the kernel trick in this formulation for classifying nonlinear multiple instance data. We also propose a branch and bound algorithm and present computational results on publicly available benchmark data sets. Our approach outperforms a leading commercial solver in terms of the best integer solution and optimality gap in the majority of image annotation and molecular activity prediction test cases.