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Maximum Margin MultiLabel Structured Prediction
 In NIPS
, 2011
"... We study multilabel prediction for structured output sets, a problem that occurs, for example, in object detection in images, secondary structure prediction in computational biology, and graph matching with symmetries. Conventional multilabel classification techniques are typically not applicable ..."
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Cited by 6 (1 self)
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accuracy, or leads to infeasible optimization problems. In this work we derive a maximummargin training formulation for multilabel structured prediction that remains computationally tractable while achieving high prediction accuracy. It also shares most beneficial properties with singlelabel maximummargin
Multilabel classification: An overview
 Int J Data Warehousing and Mining
, 2007
"... Nowadays, multilabel classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multilabel classification, organizes the sparse related literature into a ..."
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Cited by 219 (9 self)
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into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multilabel nature of a data set.
Large margin methods for structured and interdependent output variables
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
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Cited by 612 (12 self)
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to accomplish this, we propose to appropriately generalize the wellknown notion of a separation margin and derive a corresponding maximummargin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm
A Kernel Method for MultiLabelled Classification
 In Advances in Neural Information Processing Systems 14
, 2001
"... This article presents a Support Vector Machine (SVM) like learning system to handle multilabel problems. Such problems are usually decomposed into many twoclass problems but the expressive power of such a system can be weak [5, 7]. We explore a new direct approach. It is based on a large margi ..."
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Cited by 228 (0 self)
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This article presents a Support Vector Machine (SVM) like learning system to handle multilabel problems. Such problems are usually decomposed into many twoclass problems but the expressive power of such a system can be weak [5, 7]. We explore a new direct approach. It is based on a large
Maxmargin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ..."
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Cited by 594 (15 self)
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. In this paper, we present a new framework that combines the advantages of both approaches: Maximum margin Markov (M 3) networks incorporate both kernels, which efficiently deal with highdimensional features, and the ability to capture correlations in structured data. We present an efficient algorithm
Classifier Chains for Multilabel Classification
"... Abstract. The widely known binary relevance method for multilabel classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its labelindependence assumption. Instead, most current methods invest considerable ..."
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Cited by 153 (12 self)
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computational complexity. Empirical evaluation over a broad range of multilabel datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and stateoftheart methods, both in terms of predictive performance and time complexity. 1
A training algorithm for optimal margin classifiers
 PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
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Cited by 1848 (44 self)
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A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters
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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, capitalization, formatting, partofspeech), and defines the conditional probability of state sequences given observation sequences. It does this by using the maximum entropy framework to fit a set of exponential models that represent the probability of a state given an observation and the previous state. We
Mining multilabel data
 In Data Mining and Knowledge Discovery Handbook
, 2010
"... A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such d ..."
Results 1  10
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1,651,555