Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

We present experiments demonstrating that some other form of capacity control, different
from network size, plays a central role in learning multilayer feed-forward networks. We …

Three important properties of a classification machinery are i) the system preserves the core
information of the input data; ii) the training examples convey information about unseen …

Kernel ridge regression is a simple yet powerful technique for nonparametric regression
whose computation amounts to solving a linear system. This system is usually dense and …

The problem of extracting features from given data is of critical importance for successful
application of machine learning. Feature extraction, as usually understood, seeks an optimal …

Contemporary deep neural networks exhibit impressive results on practical problems. These
networks generalize well although their inherent capacity may extend significantly beyond …

Remarkable recent success of deep neural networks has not been easy to analyze
theoretically. It has been particularly hard to disentangle relative significance of architecture …

Designing an effective criterion to select a subset of features is a challenging problem for
hyperspectral image classification. In this paper, we develop a feature selection method to …

Bayesian optimization has been successfully used to optimize complex black-box functions
whose evaluations are expensive. In many applications, like in deep learning and predictive …

Kernel methods are ubiquitous tools in machine learning. They have proven to be effective
in many domains and tasks. Yet, kernel methods often require the user to select a …