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The brain strategy for online learning

Publications associées (142)

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Example-based Support Vector Machine for Drug Concentration Analysis

Giovanni De Micheli, Wenqi You Dubout

Machine learning has been largely applied to ana- lyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Feature Selection using Stochastic Search: An Application to System Identification

Ian Smith, Sandro Saitta

System identification using multiple-model strategies may involve thousands of models with several parameters. However, only a few models are close to the correct model. A key task involves finding which parameters are important for explaining candidate mo ...

American Society of Civil Engineers2010

Learning from Candidate Labeling Sets

Jie Luo

In many real world applications we do not have access to fully-labeled training data, but only to a list of possible labels. This is the case, e.g., when learning visual classifiers from images downloaded from the web, using just their text captions or tag ...

MIT Press2010

On-line independent support vector machines

Barbara Caputo

Support vector machines (SVMs) are one of the most successful algorithms for classification. However, due to their space and time requirements, they are not suitable for on-line learning, that is, when presented with an endless stream of training observati ...

2010

Enhancement of Geomatics Education by Using Internet Based Techniques

Pierre-Yves Gilliéron, Bertrand Merminod, Arnaud Deshogues

The Swiss Federal Institute of Technology in Lausanne (EPFL) has introduced the Moodle Learning Management System (LMS) in 2005. Since then, the number of teachers and users has increased rapidly. With more than 350 active courses hosted on Moodle for this ...

2010

Multistream Speaker Diarization beyond Two Acoustic Feature Streams

Hervé Bourlard, Fabio Valente, Deepu Vijayasenan

Speaker diarization for meetings data are recently converging towards multistream systems. The most common complementary features used in combination with MFCC are Time Delay of Arrival (TDOA). Also other features have been proposed although, there are no ...

2010

Joint Pose Estimator and Feature Learning for Object Detection

Pascal Fua, François Fleuret, Karim Ali, David Hasler

A new learning strategy for object detection is presented. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform ba ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Information theoretic feature extraction for audio-visual speech recognition

Jean-Philippe Thiran, Mihai Gurban

The problem of feature selection has been thoroughly analyzed in the context of pattern classification, with the purpose of avoiding the curse of dimensionality. However, in the context of multimodal signal processing, this problem has been studied less. O ...

2009

An online framework for learning novel concepts over multiple cues

Barbara Caputo, Jie Luo

We propose an online learning algorithm to tackle the problem of learning under limited computational resources in a teacher-student scenario, over multiple visual cues. For each separate cue, we train an online learning algorithm that sacrifices performan ...

2009

SELECTING RELEVANT VISUAL FEATURES FOR SPEECHREADING

Jean-Philippe Thiran, Mihai Gurban, Virginia Estellers Casas

A quantitative measure of relevance is proposed for the task of constructing visual feature sets which are at the same time relevant and compact. A feature's relevance is given by the amount of information that it contains about the problem, while compactn ...

2009