Publications related to Loss functions for classification

Multivariate Boosting with Look-Up Tables for Face Processing

This thesis proposes a novel unified boosting framework. We apply this framework to the several face processing tasks, face detection, facial feature localisation, and pose classification, and use the same boosting algorithm and the same pool of features ( ...

EPFL2012

Distributed classification of multiple observation sets by consensus

Pascal Frossard, Effrosyni Kokiopoulou

We consider the problem of distributed classification of multiple observations of the same object that are collected in an ad hoc network of vision sensors. Assuming that each sensor captures a different observation of the same object, the problem is to cl ...

Institute of Electrical and Electronics Engineers2011

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Jie Luo

Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...

2011

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Jie Luo

Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...

Idiap2011

Accelerated Wavelet-Regularized Deconvolution For 3-D Fluorescence Microcopy

Michaël Unser, Matthieu Guerquin-Kern, Ilker Bayram, Raquel Terrés Cristofani

Modern deconvolution algorithms are often specified as minimization problems involving a non-quadratic regularization functional. When the latter is a wavelet-domain l(1)-norm that favors sparse solutions, the problem can be solved by a simple iterative sh ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Accelerated Wavelet-Regularized Deconvolution for 3-D Fluorescence Microcopy

Michaël Unser, Matthieu Guerquin-Kern, Ilker Bayram, Raquel Terrés Cristofani

Modern deconvolution algorithms are often specified as minimization problems involving a non-quadratic regularization functional. When the latter is a wavelet-domain

ℓ _{ 1 }

-norm that favors sparse solutions, the problem can be solved by a simple it ...

IEEE2010

Geometry-aware analysis of high-dimensional visual information sets

Effrosyni Kokiopoulou

Over the past few decades we have been experiencing a data explosion; massive amounts of data are increasingly collected and multimedia databases, such as YouTube and Flickr, are rapidly expanding. At the same time rapid technological advancements in mobil ...

EPFL2009

A Proposal for a Kernel-based Algorithm for Large Vocabulary Continuous Speech Recognition

We present a proposal of a kernel-based model for large vocabulary continuous speech recognizer. The continuous speech recognition is described as a problem of finding the best phoneme sequence and its best time span, where the phonemes are generated from ...

John Wiley and Sons2009

Hierarchical Integration of Phonetic and Lexical Knowledge in Phone Posterior Estimation

Hervé Bourlard, Hamed Ketabdar

Phone posteriors has recently quite often used (as additional features or as local scores) to improve state-of-the-art automatic speech recognition (ASR) systems. Usually, better phone posterior estimates yield better ASR performance. In the present paper ...

2008

A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces

Touradj Ebrahimi, Ulrich Hoffmann

Gradient boosting is a machine learning method, that builds one strong classifier from many weak classifiers. In this work, an algorithm based on gradient boosting is presented, that detects event-related potentials in single electroencephalogram (EEG) tri ...

SPIE2005

Loss functions for classification

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Multivariate Boosting with Look-Up Tables for Face Processing

Distributed classification of multiple observation sets by consensus

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Accelerated Wavelet-Regularized Deconvolution For 3-D Fluorescence Microcopy

Accelerated Wavelet-Regularized Deconvolution for 3-D Fluorescence Microcopy

Geometry-aware analysis of high-dimensional visual information sets

A Proposal for a Kernel-based Algorithm for Large Vocabulary Continuous Speech Recognition

Hierarchical Integration of Phonetic and Lexical Knowledge in Phone Posterior Estimation

A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces

Multivariate Boosting with Look-Up Tables for Face Processing

Hierarchical Integration of Phonetic and Lexical Knowledge in Phone Posterior Estimation

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Accelerated Wavelet-Regularized Deconvolution for 3-D Fluorescence Microcopy

A Proposal for a Kernel-based Algorithm for Large Vocabulary Continuous Speech Recognition

A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces

Distributed classification of multiple observation sets by consensus

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Accelerated Wavelet-Regularized Deconvolution For 3-D Fluorescence Microcopy

Geometry-aware analysis of high-dimensional visual information sets