Results 1 - 10 of 906

Maximum-Margin Feature Combination for Detection and Categorization

by Gökhan H. Bakır, Mingrui Wu, Jan Eichhorn
"... 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 ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
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: minimum-bound vs. maximum-margin classifiers

by Amiran Ambroladze, Emilio Parrado-hernández, John Shawe-taylor , 2005
"... In this paper we introduce a new algorithm to train Support Vector Machines that aims at the minimisation of the PAC-Bayes 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 PAC-Bayes 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

by Ben Taskar, Simon Lacoste-julien, Michael I. Jordan - Journal of Machine Learning Research , 2006
"... We present a simple and scalable algorithm for maximum-margin estimation of structured output models, including an important class of Markov networks and combinatorial models. We formulate the estimation problem as a convex-concave saddle-point problem that allows us to use simple projection methods ..."
Abstract - Cited by 59 (2 self) - Add to MetaCart
We present a simple and scalable algorithm for maximum-margin estimation of structured output models, including an important class of Markov networks and combinatorial models. We formulate the estimation problem as a convex-concave saddle-point problem that allows us to use simple projection

Clock Synchronization Using Maximal Margin Estimation

by Dani E. Pinkovich, Nahum Shimkin
"... 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

by Posteriori Hyper-Parameter Estimation, Kai Krajsek, Rudolf Mester
"... Global optical flow estimation methods contain a regularization parameter (or prior and likelihood hyper-parameters 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 hyper-parameter whereas the likelihood hyper-parameter needs to be known). We adapt the marginalized maximum a posteriori (MMAP) estimator proposed in [Mohammad-Djafari(1995)] to simultaneously estimating hyper-parameters and optical flow for global motion estimation

MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification

by Jun Zhu, Amr Ahmed, Eric P. Xing
"... 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 max-margin supervised topic model for both continuous and categorical response variab ..."
Abstract - Cited by 93 (27 self) - Add to MetaCart
variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin 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 . . .

by Song Chun Zhu, Yingnian Wu, David Mumford - 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 ..."
Abstract - Cited by 233 (16 self) - Add to MetaCart
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

by M. I. Layton, M. J. F. Gales , 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 maximum-mar ..."
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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 maximum-margin

Marginal maximum likelihood estimation for the ordered partition model

by Mark Wilson, Raymond J. Adams - Journal of Educational Statistics , 1993
"... This article describes a marginal maximum likelihood (MML) estima-tion 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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This article describes a marginal maximum likelihood (MML) estima-tion 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

by C. Longworth, M. J. F. Gales - 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 ..."
Abstract - Cited by 7 (3 self) - Add to MetaCart
-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 MMI-MAP, the A-GMM system gave an Equal Error Rate (EER
Results 1 - 10 of 906