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A framework for multiple-instance learning

by Oded Maron - In Advances in Neural Information Processing Systems , 1998
"... Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled ..."
Abstract - Cited by 259 (2 self) - Add to MetaCart
Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled

Support vector machines for multiple-instance learning

by Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann - Advances in Neural Information Processing Systems 15 , 2003
"... This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state-of-the ..."
Abstract - Cited by 309 (2 self) - Add to MetaCart
This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state

Multiple-instance active learning

by Burr Settles, Mark Craven, Soumya Ray - In Advances in Neural Information Processing Systems (NIPS , 2008
"... We present a framework for active learning in the multiple-instance (MI) setting. In an MI learning problem, instances are naturally organized into bags and it is the bags, instead of individual instances, that are labeled for training. MI learners assume that every instance in a bag labeled negativ ..."
Abstract - Cited by 111 (7 self) - Add to MetaCart
We present a framework for active learning in the multiple-instance (MI) setting. In an MI learning problem, instances are naturally organized into bags and it is the bags, instead of individual instances, that are labeled for training. MI learners assume that every instance in a bag labeled

Multiple instance regression

by Soumya Ray - In Proceedings of the 18th International Conference on Machine Learning , 2001
"... This paper introduces multiple instance regression, a variant of multiple regression in which each data point may be described by more than one vector of values for the independent variables. The goals of this work are to (1) understand the computational complexity of the multiple instance regressio ..."
Abstract - Cited by 52 (3 self) - Add to MetaCart
This paper introduces multiple instance regression, a variant of multiple regression in which each data point may be described by more than one vector of values for the independent variables. The goals of this work are to (1) understand the computational complexity of the multiple instance

Multiple instance boosting for object detection

by Paul Viola, John C. Platt, Cha Zhang - In NIPS 18 , 2006
"... A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set. We can create such an object detector by taking the architecture of the Viola-Jones detector cascade and training it with a new variant of boosting that we call MIL-Boost. MI ..."
Abstract - Cited by 179 (10 self) - Add to MetaCart
-Boost. MILBoost uses cost functions from the Multiple Instance Learning literature combined with the AnyBoost framework. We adapt the feature selection criterion of MILBoost to optimize the performance of the Viola-Jones cascade. Experiments show that the detection rate is up to 1.6 times better using MILBoost

Visual Tracking with Online Multiple Instance Learning

by Boris Babenko, Ming-hsuan Yang, Serge Belongie , 2009
"... In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called “tracking by detection” have been shown to give promising results at realtime speeds. These methods train a discriminative classifier in an online ..."
Abstract - Cited by 256 (20 self) - Add to MetaCart
the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We present a novel online MIL algorithm for object tracking

On Generalized Multiple-Instance Learning

by Stephen Scott, Jun Zhang, Joshua Brown - International Journal of Computational Intelligence and Applications , 2003
"... We describe a generalization of the multiple-instance learning model in which a bag's label is not based on a single instance's proximity to a single target point. Rather, a bag is positive if and only if it contains a collection of instances, each near one of a set of target points. We li ..."
Abstract - Cited by 30 (7 self) - Add to MetaCart
We describe a generalization of the multiple-instance learning model in which a bag's label is not based on a single instance's proximity to a single target point. Rather, a bag is positive if and only if it contains a collection of instances, each near one of a set of target points. We

Multiple-Instance Learning for Natural Scene Classification

by Oded Maron, Aparna Lakshmi Ratan - In The Fifteenth International Conference on Machine Learning , 1998
"... Multiple-Instance learning is a way of modeling ambiguity in supervised learning examples. Each example is a bag of instances, but only the bag is labeled - not the individual instances. A bag is labeled negative if all the instances are negative, and positive if at least one of the instances in pos ..."
Abstract - Cited by 224 (2 self) - Add to MetaCart
Multiple-Instance learning is a way of modeling ambiguity in supervised learning examples. Each example is a bag of instances, but only the bag is labeled - not the individual instances. A bag is labeled negative if all the instances are negative, and positive if at least one of the instances

Multiple Instance Ranking

by Charles Bergeron, Jed Zaretzki, Curt Breneman, Kristin P. Bennett
"... This paper introduces a novel machine learning model called multiple instance ranking (MIRank) that enables ranking to be performed in a multiple instance learning setting. The motivation for MIRank stems from the hydrogen abstraction problem in computational chemistry, that of predicting the group ..."
Abstract - Cited by 11 (0 self) - Add to MetaCart
This paper introduces a novel machine learning model called multiple instance ranking (MIRank) that enables ranking to be performed in a multiple instance learning setting. The motivation for MIRank stems from the hydrogen abstraction problem in computational chemistry, that of predicting the group

EM-DD: An Improved Multiple-Instance Learning Technique

by Qi Zhang, Sally A. Goldman - In Advances in Neural Information Processing Systems , 2001
"... We present a new multiple-instance (MI) learning technique (EMDD) that combines EM with the diverse density (DD) algorithm. ..."
Abstract - Cited by 160 (5 self) - Add to MetaCart
We present a new multiple-instance (MI) learning technique (EMDD) that combines EM with the diverse density (DD) algorithm.
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