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CuttingPlane Training of Structural SVMs
, 2007
"... Discriminative training approaches like structural SVMs have shown much promise for building highly complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. However, current training algorithms are computationally expensive or i ..."
Abstract

Cited by 318 (10 self)
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Discriminative training approaches like structural SVMs have shown much promise for building highly complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. However, current training algorithms are computationally expensive
Learning Structural SVMs with Latent Variables
"... It is well known in statistics and machine learning that the combination of latent (or hidden) variables and observed variables offer more expressive power than models with observed variables alone. Latent variables ..."
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Cited by 213 (2 self)
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It is well known in statistics and machine learning that the combination of latent (or hidden) variables and observed variables offer more expressive power than models with observed variables alone. Latent variables
SUPERVISED CLUSTERING WITH STRUCTURAL SVMs
, 2009
"... Supervised clustering is the problem of training clustering methods to produce desirable clusterings. Given sets of items and complete clusterings over these sets, a supervised clustering algorithm learns how to cluster future sets of items in a similar fashion, typically by changing the underlying ..."
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similarity measure between item pairs. This work presents a general approach for training clustering methods such as correlation clustering and kmeans/spectral clustering able to optimize to taskspecific performance criteria using structural SVMs. We empirically and theoretically analyze our supervised
Training structural SVMs when exact inference is intractable
 IN: PROC. INTL. CONF. ON MACHINE LEARNING (ICML
, 2008
"... While discriminative training (e.g., CRF, structural SVM) holds much promise for machine translation, image segmentation, and clustering, the complex inference these applications require make exact training intractable. This leads to a need for approximate training methods. Unfortunately, knowledge ..."
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Cited by 139 (7 self)
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about how to perform efficient and effective approximate training is limited. Focusing on structural SVMs, we provide and explore algorithms for two different classes of approximate training algorithms, which we call undergenerating (e.g., greedy) and overgenerating (e.g., relaxations) algorithms. We
Predicting Diverse Subsets Using Structural SVMs
"... In many retrieval tasks, one important goal involves retrieving a diverse set of results (e.g., documents covering a wide range of topics for a search query). First of all, this reduces redundancy, effectively showing more information with the presented results. Secondly, queries are often ambiguous ..."
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Cited by 61 (11 self)
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functions, we present a method that explicitly trains to diversify results. In particular, we formulate the learning problem of predicting diverse subsets and derive a training method based on structural SVMs. 1.
Chinese Event Descriptive Clause Splitting with Structured SVMs
"... Abstract: Chinese event descriptive clause splitting is the task of splitting a complex Chinese sentence into several clauses. In this paper, we present a discriminative approach for Chinese event descriptive clause splitting task. By formulating the Chinese clause splitting task as a sequence label ..."
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Cited by 1 (0 self)
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labeling problem, we apply the structured SVMs model to Chinese clause splitting. Compared with other two baseline systems, our approach gives much better performance. 1
Exact and Approximate Inference for Annotating Graphs with Structural SVMs ⋆
"... Abstract. Training processes of structured prediction models such as structural SVMs involve frequent computations of the maximumaposteriori (MAP) prediction given a parameterized model. For specific output structures such as sequences or trees, MAP estimates can be computed efficiently by dynamic ..."
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Cited by 1 (0 self)
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Abstract. Training processes of structured prediction models such as structural SVMs involve frequent computations of the maximumaposteriori (MAP) prediction given a parameterized model. For specific output structures such as sequences or trees, MAP estimates can be computed efficiently by dynamic
Static Analysis of Binary Executables Using Structural SVMs
"... We cast the problem of identifying basic blocks of code in a binary executable as learning a mapping from a byte sequence to a segmentation of the sequence. In general, inference in segmentation models, such as semiCRFs, can be cubic in the length of the sequence. By taking advantage of the structu ..."
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Cited by 1 (0 self)
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of the structure of our problem, we derive a lineartime inference algorithm which makes our approach practical, given that even small programs are tens or hundreds of thousands bytes long. Furthermore, we introduce two loss functions which are appropriate for our problem and show how to use structural SVMs
Dual coordinate solvers for largescale structural SVMs
"... This manuscript describes a method for training linear SVMs (including binary SVMs, SVM regression, and structural SVMs) from large, outofcore training datasets. Current strategies for largescale learning fall into one of two camps; batch algorithms which solve the learning problem given a finite ..."
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This manuscript describes a method for training linear SVMs (including binary SVMs, SVM regression, and structural SVMs) from large, outofcore training datasets. Current strategies for largescale learning fall into one of two camps; batch algorithms which solve the learning problem given a
A Sequential Dual Method for Structural SVMs
"... In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to computational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms ..."
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Cited by 4 (1 self)
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algorithms. We consider the problem of structured classification. In the last few years, large margin classifiers like support vector machines (SVMs) have shown much promise for structured output learning. The related optimization problem is a convex quadratic program (QP) with a large number of constraints
Results 1  10
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