Linear classification is a useful tool in machine learning and data mining. For some data in a
rich dimensional space, the performance (ie, testing accuracy) of linear classifiers has …

The goal of supervised feature selection is to find a subset of input features that are
responsible for predicting output values. The least absolute shrinkage and selection …

In this paper, we propose three approaches for the estimation of the Tucker decomposition
of multi-way arrays (tensors) from partial observations. All approaches are formulated as …

We develop new stochastic optimization methods that are applicable to a wide range of
structured regularizations. Basically our methods are combinations of basic stochastic …

Mutual information (MI) is useful for detecting statistical independence between random
variables, and it has been successfully applied to solving various machine learning …

Reinforcement learning (RL) is a framework for decision making in unknown environments
based on a large amount of data. Several practical RL applications for business intelligence …

Change-point detection is the problem of finding abrupt changes in time-series, and it is
attracting a lot of attention in the artificial intelligence and data mining communities. In this …

Feature selection is a challenging and increasing important task in the machine learning
and data mining community. According to different learning scenarios such as the …

We propose a new Sparse Bayesian Learning (SBL) algorithm that can deliver fast, block-
sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of …

The ability to automatically detect certain types of cells or cellular subunits in microscopy
images is of significant interest to a wide range of biomedical research and clinical practices …