Kernel methods

Related publications (26)

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The Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-based Hand Movement Classification

Barbara Caputo

There has been increasing interest in applying learning algorithms to improve the dexterity of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on the second iteration of the publicly released NinaPro database, which cont ...

2014

Superfluid–insulator transition in weakly interacting disordered Bose gases: a kernel polynomial approach

Vincenzo Savona, Pierre Lugan, Joseph Saliba

An iterative scheme based on the kernel polynomial method is devised for the efficient computation of the one-body density matrix of weakly interacting Bose gases within Bogoliubov theory. This scheme is used to analyze the coherence properties of disorder ...

Institute of Physics (IoP) and Deutsche Physikalische Gesellschaft2013

Automatic Mallampati Classification Using Active Appearance Models

Jean-Philippe Thiran, Matteo Sorci, Anil Yuce, Gabriel Louis Cuendet

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, ...

2012

Learning Image Descriptors with the Boosting-Trick

Pascal Fua, Vincent Lepetit, Tomasz Piotr Trzcinski, Christos Marios Christoudias

In this paper we apply boosting to learn complex non-linear local visual feature representations, drawing inspiration from its successful application to visual object detection. The main goal of local feature descriptors is to distinctively repre- sent a s ...

2012

Structured Image Segmentation using Kernelized Features

Pascal Fua, Kevin Smith, Aurélien Lucchi, Yunpeng Li

Most state-of-the-art approaches to image segmentation formulate the problem using Conditional Random Fields. These models typically include a unary term and a pairwise term, whose parameters must be carefully chosen for optimal performance. Recently, stru ...

2012

Fast O(1) Bilateral Filtering Using Trigonometric Range Kernels

Michaël Unser, Daniel Sage, Kunal Narayan Chaudhury

It is well known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not scale with the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithm ...

IEEE2011

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

A Kernel Wrapper for Phoneme Sequence Recognition

We describe a kernel wrapper, a Mercer kernel for the task of phoneme sequence recognition which is based on operations with the Gaussian kernel, and suitable for any sequence kernel classifier. We start by presenting a kernel-based algorithm for phoneme s ...

John Wiley and Sons2009

Composite Kernel Learning

The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Multiple Kernel Learning (MKL) enables to learn the kernel, from an ensemble of ...

Omnipress2008

Composite Kernel Learning

IDIAP2008