Large-scale data mining and retrieval applications have increasingly turned to compact binary data representations as a way to achieve both fast queries and efficient data storage; many algorithms have been proposed for learning effective binary encodings. Most of these algorithms focus on learning a set of projection hyperplanes for the data and simply binarizing the result from each hyperplan...

Traffic flow (TF) prediction is an important and yet a challenging task in transportation systems, since the TF involves high nonlinearities affected by many elements. Recently, neural networks have attracted much attention for prediction, but they are commonly black boxes with complex architectures difficult to be interpreted, e.g., contributions of specific traffic elements not explicit, hard...

Least Squares Twin Support Vector Machine (LSTSVM) is an extremely efficient and fast version of SVM algorithm for binary classification. LSTSVM combines the idea of Least Squares SVM and Twin SVM in which two nonparallel hyperplanes are found by solving two systems of linear equations. Although, the algorithm is very fast and efficient in many classification tasks, it is unable to cope with tw...

in this paper, weak defining hyperplanes and the anchor points in dea, as an important subset of the set of extreme efficient points of the production possibility set (pps), are used to construct unobserved dmus and in the long run to improve the envelopment of all observed dmus. there has been a surge of articles on improving envelopment in recent years. what has been done first is in constant...

Decision table describing n objects in terms of k classification attributes and one decision attribute can be seen as a collection of n points in k-dimensional space. Each point is classified either as positive or negative. The goal of this paper is to present an efficient strategy for constructing possibly the smallest number of hyperplanes so each area surrounded by them contains a group of p...

Maximal margin classifiers are a core technology in modern machine learning. They have strong theoretical justifications and have shown empirical successes. We provide an alternative justification for maximal margin hyperplane classifiers by relating them to Bayes optimal classifiers that use Parzen windows estimations with Gaussian kernels. For any value of the smoothing parameter (the width o...

Emulation has been successfully applied across a wide variety of scientific disciplines for efficiently analysing computationally intensive models. We develop known boundary emulation strategies which utilise the fact that, many computer models, there exist hyperplanes in input parameter space model output can be evaluated far more efficiently, whether this analytically or just significantly fa...

We consider decision tables with real value conditional attributes and we present a method for extraction of features deened by hyperplanes in a multi-dimensional aane space. These new features are often more relevant for object classiication than the features deened by hyperplanes parallel to axes. The method generalizes an approach presented in 18] in case of hyperplanes not necessarily paral...

A topological hyperplane is a subspace of R (or a homeomorph of it) that is topologically equivalent to an ordinary straight hyperplane. An arrangement of topological hyperplanes in R is a finite set H such that for any nonvoid intersection Y of topological hyperplanes in H and any H ∈ H that intersects but does not contain Y , the intersection is a topological hyperplane in Y . (We also assume...