Non-linear probability weighting is an integral part of descriptive theories of choice under risk such as prospect theory. But why do these objective errors in information processing exist? Should we try to help individuals overcome their mistake of overweighting small and underweighting large probabilities? In this paper, we argue that probability weighting can be seen as a compensation for pr...

This paper is focused on a design of an algorithm for decoupling multivariable systems based on generalized predictive control (DGPC) and its application to a control of an incubator system. Proposed control algorithm uses adaptive error weighting factor in cost function in order to reduce coupling between control loops. Two techniques are presented and compared for predictive control of TITO (...

In this paper, we present an adaptive multi-lateral filtering method to increase depth map resolution. Joint bilateral upsampling (JBU) increases the resolution of a depth image considering the photometric property of corresponding high-resolution color image. The JBU uses both a spatial weighting function and a color weighting function evaluated on the data values. However, JBU causes a textur...

Local linear regression performs very well in many low-dimensional forecasting problems. In high-dimensional spaces, its performance typically decays due to the well-known "curse-of-dimensionality". A possible way to approach this problem is by varying the "shape" of the weighting kernel. In this work we suggest a new, data-driven method to estimating the optimal kernel shape. Experiments using...

Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proved incorrect, and applying uniform weight in the pathway analysis may not be an appropriate approach for the tasks like molecular classification of diseases, as genes in a functional group may have diffe...

Data collection of MRI which is sampled nonuniformly in k-space is often interpolated onto a Cartesian grid for fast reconstruction. The collected data must be properly weighted before interpolation, for accurate reconstruction. We propose a criterion for choosing the weighting function necessary to compensate for nonuniform sampling density. A numerical iterative method to find a weighting fun...

The basic idea of boosting is to increase the pattern recognition accuracy by combining classifiers which have been derived from differently weighted versions of the original training data. It has been verified in practical experiments that the resulting classification performance can be improved by increasing the weights of misclassified training samples. However, in statistical pattern recogn...