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334,779
Multi-instance multi-label learning
- Artificial Intelligence
"... In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicate ..."
Abstract
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Cited by 38 (16 self)
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In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing
Multi-Instance Dimensionality Reduction
- in Proceedings of the 24th AAAI Conference on Artificial Intelligence
"... Multi-instance learning deals with problems that treat bags of instances as training examples. In single-instance learn-ing problems, dimensionality reduction is an essential step for high-dimensional data analysis and has been studied for years. The curse of dimensionality also exists in multi-inst ..."
Abstract
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Cited by 1 (0 self)
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Multi-instance learning deals with problems that treat bags of instances as training examples. In single-instance learn-ing problems, dimensionality reduction is an essential step for high-dimensional data analysis and has been studied for years. The curse of dimensionality also exists in multi-instance
Multi-Instance Kernels
- In Proc. 19th International Conf. on Machine Learning
, 2002
"... Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently, research started investigating kernels for structured data. This paper considers kernels for multi-instance problems -- a cla ..."
Abstract
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Cited by 155 (3 self)
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Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently, research started investigating kernels for structured data. This paper considers kernels for multi-instance problems -- a
Locally weighted learning
- ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
Abstract
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
Semi-Supervised Learning Literature Survey
, 2006
"... We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a chapter ..."
Abstract
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Cited by 757 (8 self)
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We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole
spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a
Multi-instance tree learning
- In Proceedings of the 22nd International Conference on Machine Learning
, 2005
"... We introduce a novel algorithm for decision tree learning in the multi-instance setting as originally defined by Dietterich et al. It differs from existing multi-instance tree learners in a few crucial, well-motivated details. Experiments on synthetic and real-life datasets confirm the beneficial ef ..."
Abstract
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Cited by 17 (0 self)
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We introduce a novel algorithm for decision tree learning in the multi-instance setting as originally defined by Dietterich et al. It differs from existing multi-instance tree learners in a few crucial, well-motivated details. Experiments on synthetic and real-life datasets confirm the beneficial
Multi-instance clustering with applications to multi-instance prediction
- Applied Intelligence
, 2009
"... Abstract. In the setting of multi-instance learning, each object is represented by a bag composed of multiple instances instead of by a single instance in traditional learning setting. Previous works in this area only concern multi-instance prediction problems where each bag is associated with a bin ..."
Abstract
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Cited by 24 (5 self)
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Abstract. In the setting of multi-instance learning, each object is represented by a bag composed of multiple instances instead of by a single instance in traditional learning setting. Previous works in this area only concern multi-instance prediction problems where each bag is associated with a
Learning probabilistic relational models
- In IJCAI
, 1999
"... A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
Abstract
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Cited by 619 (31 self)
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A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much
Gaussian processes for machine learning
- in: Adaptive Computation and Machine Learning
, 2006
"... Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperpar ..."
Abstract
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Cited by 631 (2 self)
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Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn
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334,779
