… Some are reviewed elsewhere [16] and the own ESANN conference has devoted special
sessions to this problem [17]. The problem of finding the adequate output dimension for NLDR …

The K-fold Cross Validation (KCV) technique is one of the most used approaches by practitioners
for model selection and error estimation of classifiers. The KCV consists in splitting a …

This paper introduces a combined scattering representation for texture classification, which
is invariant to rotations and stable to deformations. A combined scattering is computed with …

After successes at image classification, segmentation is the next step towards image
understanding for neural networks. We propose a convolutional network architecture that includes …

Nonparametric dimensionality reduction (DR) techniques such as locally linear embedding
or t-distributed stochastic neighbor (t-SNE) embedding constitute standard tools to visualize …

The instantaneous assessment of high-priced liquor products with minimal sample volume
and no special preparation is an important task for quality monitoring and fraud detection. In …

The goal of a Brain-Computer Interface (BCI) is to enable communication by pure brain
activity without the need for muscle control. Recently BCIs based on code-modulated visual …

We present a machine learning system for the differential diagnosis of benign adrenocortical
adenoma (ACA) vs. malignant adrenocortical carcinoma (ACC). The data employed for the …

Random forests are effective supervised learning methods applicable to large-scale datasets.
However, the space complexity of tree ensembles, in terms of their total number of nodes, …

Human-centered computing is an emerging research field that aims to understand human
behavior and integrate users and their social context with computer systems. One of the most …