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We consider here how to assess if two classifiers, based on a set of test error results, are performing equally well. This question is often considered in the realm of sampling theory, based on classical hypothesis testing. Here we present a simple Bayesia ...
In the framework of a {B}ayesian classifier based on mixtures of gaussians, we address the problem of non-frontal face verification (when only a single (frontal) training image is available) by extending each frontal face model with artificially synthesize ...
Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...
Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...
In this paper we give an overview of our work on an asynchronous BCI (where the subject makes self-paced decisions on when to switch from a mental task to the next) that responds every 1/2 second. A local neural classifier tries to recognize three differen ...
The issues of fusion with client-dependent and confidence information have been well studied separately in biometric authentication. In this study, we propose to take advantage of both sources of information in a discriminative framework. Initially, each s ...
Changes in EEG power spectra related to the imagination of movements may be used to build up a direct communication channel between the brain and computer (Brain Computer Interface; BCI). However, for the practical implementation of a BCI device, the featu ...
Scaling down robots to miniature size introduces many new challenges including memory and program size limitations, low processor performance and low power autonomy. In this paper we describe the concept and implementation of learning of safe-wandering and ...
Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...
Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...