Concept

Styles d'apprentissage

Publications associées (326)

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Perceptual learning requires a minimal number of trials per session, but no sleep

Michael Herzog, Elisa Tartaglia, Carl Kristoffer Aberg

A common assumption in perceptual learning is that the improvement of performance basically depends on the amount of training. However, other factors such as sleep and training intensity (trials per session) have also been shown to be important. We trained ...

Association for Research in Vision and Ophthalmology2008

Requirements Engineering Education in the 21st Century, an Experiential Learning Approach

Gil Regev, Alain Wegmann

RE use in industry is hampered by a poor understanding of RE practices and their benefits. Teaching RE at the university level is therefore an important endeavor. This education can ideally be provided at the university level as an integrated part of devel ...

IEEE2008

When does roving disrupt perceptual learning?

Michael Herzog, Elisa Tartaglia, Carl Kristoffer Aberg

Training with one type of a visual stimulus usually improves performance. When observers train with two or more stimulus types presented in random order (so-called roving), performance improves for certain stimulus types but not for others. To understand w ...

2008

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

Roving in perceptual learning: stimulus interference and overlapping neural populations

Michael Herzog, Elisa Tartaglia

Performance usually improves when observers train with one type of a visual stimulus. Roving denotes the situation when, instead of one, two or more types of stimuli are presented randomly interleaved (one per trial). For some stimulus types, performance i ...

Association for Research in Vision and Ophthalmology2008

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Dario Floreano, Claudio Mattiussi, Peter Dürr, Andrea Soltoggio

memory in biological neural networks. Similarly, artificial neural networks could benefit from modulatory dynamics when facing certain types of learning problem. Here we test this hypothesis by introducing modulatory neurons to enhance or dampen neural pla ...

MIT Press2008

Discriminative Keyword Spotting

Samy Bengio, David Grangier

This paper proposes a new approach for keyword spotting, which is not based on HMMs. The proposed method employs a new discriminative learning procedure, in which the learning phase aims at maximizing the area under the ROC curve, as this quantity is the m ...

2007

Intrinsically motivated machines

Frédéric Kaplan

Children seem intrinsically motivated to manipulate, to explore, to test, to learn and they look for activities and situations that provide such learning opportunities. Inspired by research in developmental psychology and neuroscience, some researchers hav ...

Springer Verlag2007

Slowness: An Objective for Spike-Timing-Dependent Plasticity?

Slow Feature Analysis (SFA) is an efficient algorithm for learning input-output functions that extract the most slowly varying features from a quickly varying signal. It has been successfully applied to the unsupervised learning of translation-, rotation-, ...

Public Library of Science2007