Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth 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
SVM-based generalized multiple-instance learning via approximate box counting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
MILES: Multiple-Instance Learning via Embedded Instance Selection
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
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
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
Sparse Spectrum Gaussian Process Regression
The Journal of Machine Learning Research
MILIS: Multiple Instance Learning with Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering
IEEE Transactions on Neural Networks
Robust multiple-instance learning with superbags
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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Multiple instance learning (MIL) is a binary classification problem with loosely supervised data where a class label is assigned only to a bag of instances indicating presence/absence of positive instances. In this paper we introduce a novel MIL algorithm using Gaussian processes (GP). The bag labeling protocol of the MIL can be effectively modeled by the sigmoid likelihood through the max function over GP latent variables. As the non-continuous max function makes exact GP inference and learning infeasible, we propose two approximations: the soft-max approximation and the introduction of witness indicator variables. Compared to the state-of-the-art MIL approaches, especially those based on the Support Vector Machine, our model enjoys two most crucial benefits: (i) the kernel parameters can be learned in a principled manner, thus avoiding grid search and being able to exploit a variety of kernel families with complex forms, and (ii) the efficient gradient search for kernel parameter learning effectively leads to feature selection to extract most relevant features while discarding noise. We demonstrate that our approaches attain superior or comparable performance to existing methods on several real-world MIL datasets including large-scale content-based image retrieval problems.