Monte-Carlo approximation algorithms for enumeration problems
Journal of Algorithms
Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Agnostic learning of geometric patterns
Journal of Computer and System Sciences
A Kernel Approach for Learning from almost Orthogonal Patterns
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth 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
Multiple instance learning of real valued data
The Journal of Machine Learning Research
Supervised versus multiple instance learning: an empirical comparison
ICML '05 Proceedings of the 22nd international conference on Machine learning
A regularization framework for multiple-instance learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Solving multi-instance problems with classifier ensemble based on constructive clustering
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Evaluation of Localized Semantics: Data, Methodology, and Experiments
International Journal of Computer Vision
Multi-objective Genetic Programming for Multiple Instance Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
Learning with labeled sessions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
DS '09 Proceedings of the 12th International Conference on Discovery Science
Multiple instance learning with genetic programming for web mining
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
The Knowledge Engineering Review
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
Adaptive kernel diverse density estimate for multiple instance learning
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Unsupervised multiple-instance learning for functional profiling of genomic data
ECML'06 Proceedings of the 17th European conference on Machine Learning
Multiple-Instance learning via random walk
ECML'06 Proceedings of the 17th European conference on Machine Learning
Constructing target concept in multiple instance learning using maximum partial entropy
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Information Sciences: an International Journal
Class-dependent dissimilarity measures for multiple instance learning
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Multiple instance learning via Gaussian processes
Data Mining and Knowledge Discovery
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The multiple-instance learning (MIL) model has been very successful in application areas such as drug discovery and content-based image-retrieval. Recently, a generalization of this model and an algorithm for this generalization were introduced, showing significant advantages over the conventional MIL model in certain application areas. Unfortunately, this algorithm is inherently inefficient, preventing scaling to high dimensions. We reformulate this algorithm using a kernel for a support vector machine, reducing its time complexity from exponential to polynomial. Computing the kernel is equivalent to counting the number of axis-parallel boxes in a discrete, bounded space that contain at least one point from each of two multisets P and Q. We show that this problem is #P-complete, but then give a fully polynomial randomized approximation scheme (FPRAS) for it. Finally, we empirically evaluate our kernel.