— We propose a powerful symmetric kernel classifier for nonlinear detection in challenging rank-deficient multipleantenna aided communication systems. By exploiting the inheren...
Sheng Chen, Andreas Wolfgang, Chris J. Harris, Laj...
— Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression tec...
— To approximate complex data, we propose new type of low-dimensional “principal object”: principal cubic complex. This complex is a generalization of linear and nonlinear pr...
Alexander N. Gorban, Neil R. Sumner, Andrei Yu. Zi...
— The Self-Organising Map is a popular unsupervised neural network model which has successfully been used for clustering various kinds of data. To help in understanding the infl...
— In this paper we provide experimental results and extensions to our previous theoretical findings concerning the combination of forecasts that have been diversified by three ...
— This paper suggests a constructive fuzzy system modeling for time series prediction. The model proposed is based on Takagi-Sugeno system and it comprises two phases. First, a f...
—Perception, prediction and generation of sequences is a fundamental aspect of human behavior and depends on the ability to detect serial order. This paper presents a plausible m...
— We present a methodology to analyze Multiple Classifiers Systems (MCS) performance, using the diversity concept. The goal is to define an alternative approach to the convention...