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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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on the developed theory were proposed. This made statistical learning theory not only a tool for the theoretical analysis but also a tool for creating practical algorithms for estimating multidimensional functions. This article presents a very general overview of statistical learning theory including both
On Generalized MultipleInstance Learning
 International Journal of Computational Intelligence and Applications
, 2003
"... We describe a generalization of the multipleinstance 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 ..."
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Cited by 32 (7 self)
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We describe a generalization of the multipleinstance 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
An extended kernel for generalized multipleinstance learning
 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004
, 2004
"... The multipleinstance learning (MIL) model has been successful in numerous application areas. Recently, a generalization of this model and an algorithm for it were introduced, showing significant advantages over the conventional MIL model on certain application areas. Unfortunately, that algorithm i ..."
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Cited by 12 (1 self)
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The multipleinstance learning (MIL) model has been successful in numerous application areas. Recently, a generalization of this model and an algorithm for it were introduced, showing significant advantages over the conventional MIL model on certain application areas. Unfortunately, that algorithm
A faster algorithm for generalized multipleinstance learning
 in Proc. 17th Int. Florida Artif. Intell. Res. Soc. Conf
"... In our prior work, we introduced a generalization of the multipleinstance learning (MIL) 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 tar ..."
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Cited by 12 (3 self)
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In our prior work, we introduced a generalization of the multipleinstance learning (MIL) 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
SVMBased Generalized MultipleInstance Learning
 In Proceedings of the TwentyFirst International Conference on Machine Learning
, 2004
"... The multipleinstance learning (MIL) model has been very successful in application areas such as drug discovery and contentbased imageretrieval. ..."
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The multipleinstance learning (MIL) model has been very successful in application areas such as drug discovery and contentbased imageretrieval.
A Faster Algorithm for Generalized MultipleInstance Learning
"... In our prior work, we introduced a generalization of the multipleinstance learning (MIL) 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 ..."
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In our prior work, we introduced a generalization of the multipleinstance learning (MIL) 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
Protein Function Classification with Generalized Multiple Instance Learning
, 2004
"... We apply the generalized multipleinstance learning (GMIL) model and its learning algorithms, GMIL2, and recently developed kernelbased GMIL, to the protein function classification problem. With the advancement in DNA sequencing techniques, new putative proteins are added to databases much faste ..."
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Cited by 2 (0 self)
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We apply the generalized multipleinstance learning (GMIL) model and its learning algorithms, GMIL2, and recently developed kernelbased GMIL, to the protein function classification problem. With the advancement in DNA sequencing techniques, new putative proteins are added to databases much
SVMbased generalized multipleinstance learning via approximate box counting
 In Proceedings of the TwentyFirst International Conference on Machine Learning
, 2004
"... The multipleinstance learning (MIL) model has been very successful in application areas such as drug discovery and contentbased imageretrieval. Recently, a generalization of this model and an algorithm for this generalization were introduced, showing significant advantages over the conventional MI ..."
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Cited by 36 (2 self)
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The multipleinstance learning (MIL) model has been very successful in application areas such as drug discovery and contentbased imageretrieval. Recently, a generalization of this model and an algorithm for this generalization were introduced, showing significant advantages over the conventional
MICCLLR: A Generalized MultipleInstance Learning Algorithm Using Class Conditional Log Likelihood Ratio
"... We propose a new generalized multipleinstance learning (MIL) algorithm, MICCLLR (multipleinstance class conditional likelihood ratio), that converts the MI data into a single metainstance data allowing any propositional classifier to be applied. Experimental results on a wide range of MI data set ..."
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Cited by 3 (1 self)
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We propose a new generalized multipleinstance learning (MIL) algorithm, MICCLLR (multipleinstance class conditional likelihood ratio), that converts the MI data into a single metainstance data allowing any propositional classifier to be applied. Experimental results on a wide range of MI data
A framework for multipleinstance learning
 In Advances in Neural Information Processing Systems
, 1998
"... Multipleinstance 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 ..."
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Cited by 264 (2 self)
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negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multipleinstance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection
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