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Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization

Publications associées (74)

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Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

Sleep and Wake Classification With ECG and Respiratory Effort Signals

Dario Floreano, Claudio Mattiussi, Walter Karlen

We describe a method for the online classification of sleep/wake states based on cardiorespiratory signals produced by wearable sensors. The method was conceived in view of its applicability to a wearable sleepiness monitoring device. The method uses a Fas ...

2009

Nonideal sampling and regularized interpolation of noisy data

Sathish Ramani

Conventional sampling (Shannon's sampling formulation and its approximation-theoretic counterparts) and interpolation theories provide effective solutions to the problem of reconstructing a signal from its samples, but they are primarily restricted to the ...

EPFL2009

Partial least-squares regression with unlabeled data

Dominique Bonvin, Michael Amrhein, Pamandeep Singh Gujral

It is well known that the prediction errors from principal component regression (PCR) and partial least-squares regression (PLSR) can be reduced by using both labeled and unlabeled data for stabilizing the latent subspaces in the calibration step. An appro ...

2009

Analysis of Confusion Matrix to Combine Evidence for Phoneme Recognition

Hynek Hermansky, Joel Praveen Pinto

In this work we analyze and combine evidences from different classifiers for phoneme recognition using information from the confusion matrices. Speech signals are processed to extract the Perceptual Linear Prediction (PLP) and Multi-RASTA (MRASTA) features ...

IDIAP2007

More Efficiency in Multiple Kernel Learning

An efficient and general multiple kernel learning (MKL) algorithm has been recently proposed by \singleemcite{sonnenburg_mkljmlr}. This approach has opened new perspectives since it makes the MKL approach tractable for large-scale problems, by iteratively ...

2007

More Efficiency in Multiple Kernel Learning

IDIAP2007

The analysis of kernel ridge regression learning algorithm.

Alexei Pozdnoukhov

The paper presents Kernel Ridge Regression, a nonlinear extension of the well known statistical model of ridge regression. New insights on the method are also presented. In particular, the connection between ridge regression and local translation-invariant ...

IDIAP2002

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

Mixture Models for Unsupervised and Supervised Learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

IDIAP2000