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906
MaximumMargin Feature Combination for Detection and Categorization
"... In this paper we are concerned with the optimal combination of features of possibly different types for detection and estimation tasks in machine vision. We propose to combine features such that the resulting classifier maximizes the margin between classes. In contrast to existing approaches which a ..."
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Cited by 2 (0 self)
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In this paper we are concerned with the optimal combination of features of possibly different types for detection and estimation tasks in machine vision. We propose to combine features such that the resulting classifier maximizes the margin between classes. In contrast to existing approaches which
Prior Support Vector Machines: minimumbound vs. maximummargin classifiers
, 2005
"... In this paper we introduce a new algorithm to train Support Vector Machines that aims at the minimisation of the PACBayes bound on the error instead of at the traditional maximisation of the margin. The training of the classifier proceeds in two stages. First some data are used to estimate a prior ..."
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In this paper we introduce a new algorithm to train Support Vector Machines that aims at the minimisation of the PACBayes bound on the error instead of at the traditional maximisation of the margin. The training of the classifier proceeds in two stages. First some data are used to estimate a prior
Structured prediction, dual extragradient and Bregman projections
 Journal of Machine Learning Research
, 2006
"... We present a simple and scalable algorithm for maximummargin estimation of structured output models, including an important class of Markov networks and combinatorial models. We formulate the estimation problem as a convexconcave saddlepoint problem that allows us to use simple projection methods ..."
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Cited by 59 (2 self)
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We present a simple and scalable algorithm for maximummargin estimation of structured output models, including an important class of Markov networks and combinatorial models. We formulate the estimation problem as a convexconcave saddlepoint problem that allows us to use simple projection
Clock Synchronization Using Maximal Margin Estimation
"... Abstract — Clock synchronization in a network is a crucial problem due to the wide use of networks with simple nodes, such as the internet, wireless sensor networks and Ad Hoc networks. We present novel algorithms for synchronization of pairs of clocks based on Maximum Margin Estimation of the offse ..."
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Abstract — Clock synchronization in a network is a crucial problem due to the wide use of networks with simple nodes, such as the internet, wireless sensor networks and Ad Hoc networks. We present novel algorithms for synchronization of pairs of clocks based on Maximum Margin Estimation
Marginalized Maximum A
"... Global optical flow estimation methods contain a regularization parameter (or prior and likelihood hyperparameters if we consider the statistical point of view) which control the tradeoff between the different constraints on the optical flow field. Although experiments (see e.g. Ng et al. [Ng an ..."
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attempts to estimate only the prior hyperparameter whereas the likelihood hyperparameter needs to be known). We adapt the marginalized maximum a posteriori (MMAP) estimator proposed in [MohammadDjafari(1995)] to simultaneously estimating hyperparameters and optical flow for global motion estimation
MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification
"... Supervised topic models utilize document’s side information for discovering predictive low dimensional representations of documents; and existing models apply likelihoodbased estimation. In this paper, we present a maxmargin supervised topic model for both continuous and categorical response variab ..."
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Cited by 93 (27 self)
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variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the maxmargin principle to train supervised topic models and estimate predictive topic representations that are arguably more suitable for prediction. We develop efficient variational methods
Filters, Random Fields and Maximum Entropy . . .
 INTERNATIONAL JOURNAL OF COMPUTER VISION
, 1998
"... This article presents a statistical theory for texture modeling. This theory combines filtering theory and Markov random field modeling through the maximum entropy principle, and interprets and clarifies many previous concepts and methods for texture analysis and synthesis from a unified point of vi ..."
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Cited by 233 (16 self)
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is called feature extraction. (2) The maximum entropy principle is employed to derive a distribution p(I), which is restricted to have the same marginal distributions as those in (1). This p(I) is considered as an estimate of f (I). This step is called feature fusion. A stepwise algorithm is proposed
Maximum margin training of generative kernels
, 2004
"... Generative kernels, a generalised form of Fisher kernels, are a powerful form of kernel that allow the kernel parameters to be tuned to a specific task. The standard approach to training these kernels is to use maximum likelihood estimation. This paper describes a novel approach based on maximummar ..."
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Cited by 13 (4 self)
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Generative kernels, a generalised form of Fisher kernels, are a powerful form of kernel that allow the kernel parameters to be tuned to a specific task. The standard approach to training these kernels is to use maximum likelihood estimation. This paper describes a novel approach based on maximummargin
Marginal maximum likelihood estimation for the ordered partition model
 Journal of Educational Statistics
, 1993
"... This article describes a marginal maximum likelihood (MML) estimation algorithm for Wilson's (1990) ordered partition model (OPM), a measurement model that does not require the set of available responses to assessment tasks to be fully ordered. The model and its estimation algorithm are illust ..."
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Cited by 2 (0 self)
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This article describes a marginal maximum likelihood (MML) estimation algorithm for Wilson's (1990) ordered partition model (OPM), a measurement model that does not require the set of available responses to assessment tasks to be fully ordered. The model and its estimation algorithm
Discriminative adaptation for speaker verification
 in Proceedings InterSpeech
, 2006
"... Speaker verification is a binary classification task to determine whether a claimed speaker uttered a phrase. Current approaches to speaker verification tasks typically involve adapting a general speaker Universal Background Model (UBM), normally a Gaussian Mixture Model (GMM), to model a particular ..."
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Cited by 7 (3 self)
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specific model. The additional, augmented, parameters are discriminatively, and robustly, trained using a maximum margin estimation approach. The performance of these models is evaluated on the NIST 2002 SRE dataset. Though no gains were obtained using MMIMAP, the AGMM system gave an Equal Error Rate (EER
Results 1  10
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906