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PokeME: Applying Context-Driven Notifications to Increase Worker Engagement in Mobile Crowd-Sourcing

Related publications (22)

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Identification of Aging Faces using A-stack Classification Model

Andrzej Drygajlo, Weifeng Li

The problem of time validity of biometric models has received only a marginal attention from researchers. In this paper, we investigate the aging influence on the classifier scores of genuine client. Our studies show that as age progresses the classifier s ...

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

SimpleMKL

Multiple kernel learning (MKL) aims at simultaneously learning a kernel and the associated predictor in supervised learning settings. For the support vector machine, an efficient and general multiple kernel learning algorithm, based on semi-infinite linear ...

2008

Classification of dog barks: a machine learning approach

Frédéric Kaplan

In this study we analyzed the possible context-specific and individual-specific features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six different communicative situations was used as ...

2008

Probabilistic models for multi-classifier biometric authentication using quality measures

Jonas Richiardi

Biometric authentication can be cast as a signal processing and statistical pattern recognition problem. As such, it relies on models of signal representations that can be used to discriminate between classes. One of the assumptions typically made by the p ...

EPFL2008

Classification with class-independent quality information for biometric verification

Krzysztof Kryszczuk

Biometric identity verification systems frequently face the challenges of non-controlled conditions of data acquisition. Under such conditions biometric signals may suffer from quality degradation due to extraneous, identity-independent factors. It has bee ...

EPFL2008

Information theoretic combination of classifiers with application to face detection

Julien Meynet

Combining several classifiers has become a very active subdiscipline in the field of pattern recognition. For years, pattern recognition community has focused on seeking optimal learning algorithms able to produce very accurate classifiers. However, empiri ...

EPFL2007

Efficient Nonparametric Bayesian Modelling with Sparse Gaussian Process Approximations

Matthias Seeger

Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of powerful kernel-based models to large datasets. We present a general framework based on t ...

2006

Prior Knowledge in Kernel Methods

Alexei Pozdnoukhov

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...

École Polytechnique Fédérale de Lausanne2006

Prior knowledge in Kernel methods

Alexei Pozdnoukhov

EPFL2006

Noise Robust Discriminative Models

Samy Bengio, Quan Le

For classification problems, it is important that the classifier is trained with data which is likely to appear in the future. Discriminative models, because of their nature to focus on the boundary between classes rather than data itself, usually do not h ...

IDIAP2003