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Max-margin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based 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 ..."
Abstract
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Cited by 604 (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 high-dimensional features, and the ability to capture correlations in structured data. We present an efficient algorithm
Max-Margin Markov Networks
"... Abstract In typical classification tasks, we seek a function which assigns a label to a sin-gle object. Kernel-based approaches, such as support vector machines (SVMs), ..."
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Abstract In typical classification tasks, we seek a function which assigns a label to a sin-gle object. Kernel-based approaches, such as support vector machines (SVMs),
Chunking with Max-Margin Markov Networks*
"... Abstract. In this paper, we apply Max-Margin Markov Networks (M3Ns) to English base phrases chunking, which is a large margin approach combining both the advantages of graphical models(such as Conditional Random Fields, CRFs) and kernel-based approaches (such as Support Vector Machines, SVMs) to sol ..."
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Abstract. In this paper, we apply Max-Margin Markov Networks (M3Ns) to English base phrases chunking, which is a large margin approach combining both the advantages of graphical models(such as Conditional Random Fields, CRFs) and kernel-based approaches (such as Support Vector Machines, SVMs
Primal sparse max-margin Markov networks
- In International Conference on Knowledge Discovery and Data Mining (KDD
"... Max-margin Markov networks (M 3 N) have shown great promise in structured prediction and relational learning. Due to the KKT conditions, the M 3 N enjoys dual sparsity. However, the existing M 3 N formulation does not enjoy primal sparsity, which is a desirable property for selecting significant fea ..."
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Cited by 1 (1 self)
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Max-margin Markov networks (M 3 N) have shown great promise in structured prediction and relational learning. Due to the KKT conditions, the M 3 N enjoys dual sparsity. However, the existing M 3 N formulation does not enjoy primal sparsity, which is a desirable property for selecting significant
Accelerated Training of Max-Margin Markov Networks with Kernels
"... Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (M3N) is an effective approach. All state-of-the-art algorithms for optimizing M3N objectives take at least O(1/ɛ) number of iterations to find an ɛ accurate solution. ..."
Abstract
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Cited by 3 (0 self)
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Structured output prediction is an important machine learning problem both in theory and practice, and the max-margin Markov network (M3N) is an effective approach. All state-of-the-art algorithms for optimizing M3N objectives take at least O(1/ɛ) number of iterations to find an ɛ accurate solution
Faster Rates for Training Max-Margin Markov Networks
"... Structured output prediction is an important machine learning problem both in theory and prac-tice, and the max-margin Markov network (M3N) is an effective approach. All state-of-the-art algorithms for optimizing M3N objectives take at least O(1/) number of iterations to find an accurate solution. ..."
Abstract
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Structured output prediction is an important machine learning problem both in theory and prac-tice, and the max-margin Markov network (M3N) is an effective approach. All state-of-the-art algorithms for optimizing M3N objectives take at least O(1/) number of iterations to find an accurate solution
Contextual Classification with Functional Max-Margin Markov Networks
"... We address the problem of label assignment in computer vision: given a novel 3-D or 2-D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To this end, the Markov Random Field framework has proven to be a model of choice as it uses contextual information to yield ..."
Abstract
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Cited by 54 (9 self)
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We address the problem of label assignment in computer vision: given a novel 3-D or 2-D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To this end, the Markov Random Field framework has proven to be a model of choice as it uses contextual information
Max Margin Markov Networks (M 3 N), and an
"... In this paper, we survey the current state-ofart models for structured learning problems, including ..."
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In this paper, we survey the current state-ofart models for structured learning problems, including
Results 1 - 10
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