Discriminative Singular Spectrum Classifier with applications on bioacoustic signal recognition

Hybrid generative/discriminative classifier for unconstrained character recognition

Handwriting recognition for hand-held devices like PDAs requires very accurate and adaptive classifiers. It is such a complex classification problem that it is quite usual now to make co-operate several classification methods. In this paper, we present an original two stages recognizer. The first stage is a model-based classifier which store an exhaustive set of character models. The second sta...

Granular Singular Spectrum Analysis for Gait Recognition

Gait Recognition is a biometric application that aims to identify a person by analyzing his/her gait. It is based on the fact that people often feel that they can identify a familiar person from afar simply by recognizing the way the person walks. In this work, a qualitative approach based on Granular computing paradigm is proposed. This paradigm involves information granules, based on density ...

Automatic Recognition of Bioacoustic Sounds

Pattern Recognition of Gene Expression with Singular Spectrum Analysis

Drosophila segmentation as a model organism is one of the most highly studied. Among many maternal segmentation coordinate genes, bicoid protein pattern plays a significant role during Drosophila embryogenesis, since this gradient determines most aspects of head and thorax development. Despite the fact that several models have been proposed to describe the bicoid gradient, due to its associatio...

Polynomial Network Classifier with Discriminative Feature Extraction

The polynomial neural network, or called polynomial network classifier (PNC), is a powerful nonlinear classifier that can separate classes of complicated distributions. A method that expands polynomial terms on principal subspace has yielded superior performance. In this paper, we aim to further improve the performance of the subspace-featurebased PNC. In the framework of discriminative feature...