Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Large datasets are increasingly common and are often difficult to interpret. Principal
component analysis (PCA) is a technique for reducing the dimensionality of such datasets …

Over the past few decades, methods for classification and detection of rhythm or morphology
abnormalities in ECG signals have been widely studied. However, it lacks the …

The role of the thalamus in cortical sensory transmission is well known, but its broader role
in cognition is less appreciated. Recent studies have shown thalamic engagement in …

Working memory (WM) is a cognitive function for temporary maintenance and manipulation
of information, which requires conversion of stimulus-driven signals into internal …

Principal component analysis (PCA) is a multivariate technique that analyzes a data table in
which observations are described by several inter‐correlated quantitative dependent …

A new method for performing a nonlinear form of principal component analysis is proposed.
By the use of integral operator kernel functions, one can efficiently compute principal …

The viewpoint taken in much of this text is that PCA is mainly a descriptive tool with no need
for rigorous distributional or model assumptions. This implies that it can be used on a wide …

A comprehensive introduction to Support Vector Machines and related kernel methods. In
the 1990s, a new type of learning algorithm was developed, based on results from statistical …

In this chapter, we discuss a statistical generative model called independent component
analysis. It is basically a proper probabilistic formulation of the ideas underpinning sparse …