A method for the construction of open approximate models from vector time series Preface PhD research is a largely open proces in which a stimulating environment plays a crucial role. I am indebted to several people and institutions that shaped such an environment for me during the years since April 1990. First and for all, I would like to thank my supervisor, Christiaan Heij. He set me on the ...

We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible sequential nature) of training data arising in reinforcement learning we employ a subspace based variant of LS-SVM that sequentially processes the data and is hence especially suited for online learning. This approach is adapte...

This paper presents a simple yet powerful original approach for approximating dispersivity in porous media. The key to this approach is both use of least-squares criterion and image processing technique. Two dimensional transparent models, including horizontal and vertical configurations, under different combinations of grain sizes and pore velocities, experimented. Concentration data were obta...

Short-term forecasting of wind generation requires a model of the function for the conversion of meteorological variables (mainly wind speed) to power production. Such a power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric approach for power curve estimation, for which the model coefficients can be tracked with recursive...

We describe two methods for detecting horizontal gene transfers in the framework of the complete and partial gene transfer models. In case of a complete gene transfer model a new fast backward selection algorithm for predicting horizontal gene transfer events is presented. The latter algorithm can rely either on the metric or on the topological optimization to identify horizontal gene transfers...

nThis paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering crit...

Namyong Kim 155 In this paper, we introduce an escalator (ESC) algorithm based on the least squares (LS) criterion. The proposed algorithm is relatively insensitive to the eigenvalue spread ratio (ESR) of an input signal and has a faster convergence speed than the conventional ESC algorithms. This algorithm exploits the fast adaptation ability of least squares methods and the orthogonalization ...

In the usual optical ow detection, the gradient constraint, which expresses the relationship between the gradient of the image intensity and its motion, is combined with the least-squares criterion. This criterion means assuming that only the time derivative of the image intensity contains noise. In this paper, we assume that all image derivatives contain noise and derive a new optical ow detec...

The partial least squares (PLS) regression is a new multivariate data analysis method. In this paper, based on the ideas of PLS model and feature fusion, a new non-iterative PLS algorithm and a novel method of feature fusion are proposed. The proposed method comprises three steps: firstly, extracting two sets of feature vectors with the same pattern and establishing PLS criterion function betwe...