Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two diierent cost functions for Support Vectors: training with (i) anâ€¦ (More)

In this thesis we consider statistical learning problems and machines. A statistical learning machine tries to infer rules from a given set of examples such that it is able to make correctâ€¦ (More)

We propose a novel method for the analysis of sequential data that exhibits an inherent mode switching. In particular, the data might be a non-stationary time series from a dynamical system thatâ€¦ (More)

Brain-computer interfaces (BCIs) involve two coupled adapting systems-the human subject and the computer. In developing our BCI, our goal was to minimize the need for subject training and to imposeâ€¦ (More)

We present a method for the unsupervised segmentation of data streams originating from different unknown sources that alternate in time. We use an architecture consisting of competing neuralâ€¦ (More)

We present a framework for the unsupervised segmentation of time series. It applies to non-stationary signals originating from di erent dynamical systems which alternate in time, a phenomenon whichâ€¦ (More)

In this work we consider statistical learning problems. A learning machine aims to extract information from a set of training examples such that it is able to predict the associated label on unseenâ€¦ (More)

We present a novel framework for the analysis of time series from dynamical systems that alternate between different operating modes. The method simultaneously segments and identifies the dynamicalâ€¦ (More)

We present a method for the analysis of time series from drifting or switching dynamics. In extension to existing approaches that identify switches or drifts between stationary dynamical modes, theâ€¦ (More)