Concept

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Publications associées (274)

Perceptual Learning, Roving, and Synaptic Drift

Perceptual learning improves with most basic stimuli. Interestingly, performance does not improve when stimuli of two types are randomly presented during training (roving). For example, there is no perceptual learning when left or right bisection stimuli w ...

2012

Learning to Detect Objects with Minimal Supervision

Karim Ali

Many classes of objects can now be successfully detected with statistical machine learning techniques. Faces, cars and pedestrians, have all been detected with low error rates by learning their appearance in a highly generic manner from extensive training ...

EPFL2012

How is the Weather: Automatic Inference from Images

Martin Vetterli, Guillermo Barrenetxea Kobas, Albrecht Johannes Lindner, Feng Yang, Zichong Chen

Low-cost monitoring cameras/webcams provide unique visual information. To take advantage of the vast image dataset captured by a typical webcam, we consider the problem of retrieving weather information from a database of still images. The task is to autom ...

2012

Perceptual learning, roving and the unsupervised bias

Michael Herzog, Wulfram Gerstner, Carl Kristoffer Aberg, Nicolas Frémaux

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervise ...

Elsevier2012

Extracting Informative Textual Parts from Web Pages Containing User-Generated Content

Nikolaos Pappas

The vast amount of user-generated content on the Web has increased the need for handling the problem of automatically processing content in web pages. The segmentation of web pages and noise (non-informative segment) removal are important pre-processing st ...

ACM2012

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

Presentation and study of robustness for several methods to classify individuals based on their gene expressions

Julien Damond

Several studies have shown that it is possible to detect cancer tissues based on gene expressions using methods of machine learning. The main problem with classifying gene expression data is to obtain accurate rules that are easy to interpret and provide i ...

2011

A supervised recalibration protocol for unbiased BCI

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Robert Leeb, Serafeim Perdikis, Michele Tavella

One important source of performance degradation in BCIs is bias towards one of the mental classes. Recent literature has focused on the general problem of classification accuracy drop, identifying non-stationarity as the generating factor, thus leading to ...

2011

Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hamidreza Bayati

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors can affect the data distribution between two different experimental sessions including sensor displacemen ...

Ieee Computer Soc Press, Customer Service Center, Po Box 3014, 10662 Los Vaqueros Circle, Los Alamitos, Ca 90720-1264 Usa2011

Bayesian multitask inverse reinforcement learning

Christos Dimitrakakis

We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is ...

2011

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