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234,687
MultiInstance 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 attributevalue data. Only recently, research started investigating kernels for structured data. This paper considers kernels for multiinstance problems  a cla ..."
Abstract

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 attributevalue data. Only recently, research started investigating kernels for structured data. This paper considers kernels for multiinstance 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 ..."
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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
Learning Instance Weights in MultiInstance Learning
, 2007
"... Multiinstance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug activity prediction, fruit disease management and image classification. This t ..."
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Cited by 5 (2 self)
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Multiinstance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug activity prediction, fruit disease management and image classification
Multiinstance multilabel learning
 Artificial Intelligence
"... In this paper, we propose the MIML (MultiInstance MultiLabel 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 ..."
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Cited by 38 (16 self)
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In this paper, we propose the MIML (MultiInstance MultiLabel 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
Maximum entropy markov models for information extraction and segmentation
, 2000
"... Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many textrelated tasks, such as partofspeech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial ..."
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Cited by 554 (18 self)
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as multinomial distributions over a discrete vocabulary, and the HMM parameters are set to maximize the likelihood of the observations. This paper presents a new Markovian sequence model, closely related to HMMs, that allows observations to be represented as arbitrary overlapping features (such as word
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 766 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Multiinstance clustering with applications to multiinstance prediction
 Applied Intelligence
, 2009
"... Abstract. In the setting of multiinstance 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 multiinstance prediction problems where each bag is associated with a bin ..."
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Cited by 24 (5 self)
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Abstract. In the setting of multiinstance 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 multiinstance prediction problems where each bag is associated with a
Probabilistic Visual Learning for Object Representation
, 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixtureof ..."
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Cited by 705 (15 self)
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ofGaussians model (for multimodal distributions). These probability densities are then used to formulate a maximumlikelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection
An extensive empirical study of feature selection metrics for text classification
 J. of Machine Learning Research
, 2003
"... Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison ..."
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Cited by 483 (15 self)
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Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison
Learning probabilistic relational models
 In IJCAI
, 1999
"... A large portion of realworld 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 ..."
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Cited by 619 (31 self)
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objects. Although PRMs are significantly more expressive than standard models, such as Bayesian networks, we show how to extend wellknown statistical methods for learning Bayesian networks to learn these models. We describe both parameter estimation and structure learning — the automatic induction
Results 1  10
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234,687