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Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers

Publications associées (79)

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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

Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier

Ahmed Abdulkadir

Fully automated machine learning methods based on structural magnetic resonance imaging data can assist radiologists in the diagnosis of Alzheimer's disease (AD). These algorithms require large data sets to learn the separation of subjects with and without ...

Elsevier2011

Personalized Modeling for Drug Concentration Prediction Using Support Vector Machine

Giovanni De Micheli, Wenqi You Dubout

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propos ...

2011

Learning parameterized histogram kernels on the simplex manifold for image and action classification

Vitaly Henry Ablavsky

State-of-the-art image and action classification systems often employ vocabulary-based representations. The classification accuracy achieved with such vocabulary-based representations depends significantly on the chosen histogram-distance. In particular, w ...

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Comparison-Based Optimizers Need Comparison-Based Surrogates

Ilya Loshchilov

Taking inspiration from approximate ranking, this paper nvestigates the use of rank-based Support Vector Machine as surrogate model within CMA-ES, enforcing the invariance of the approach with respect to monotonous transformations of the fitness function. ...

2010

Online-Batch Strongly Convex Multi Kernel Learning

Barbara Caputo, Jie Luo

Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled ap- proach to combine multiple cues, and to obtain state-of-the- art performance. A general drawback of these strategies is the high computational co ...

Idiap2010

Online-Batch Strongly Convex Multi Kernel Learning

Barbara Caputo, Jie Luo

2010

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

PLSI: the true Fisher Kernel and beyond

Jean-Cédric Chappelier, Emmanuel Eckard

The Probalistic Latent Semantic Indexing model, introduced by T. Hofmann (1999), has engendered applications in numerous fields, notably document classification and information retrieval. In this context, the Fisher kernel was found to be an appropriate do ...

2009

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