Homomorphic Coordinates of Dempster’s Semigroup
Daniel, Milan
2015 - English Coordinates of belief functions on two-element frame of discernment are defined using homomorphisms of Dempster’s semigroup (the algebra of belief functions with Dempster’s rule). Three systems of the coordinates (h-f, h-f0, and coordinates based on decomposition of belief functions) are analysed with a focus to their homomorphic properties. Further, ideas of generalisation of the investigated systems of coordinates to general finite frame of discernment are presented. Keywords: belief functions; Dempster-Shafer Theory; Dempster's semigroup; homomorphism; homomorphic coordinates Fulltext is available at external website. Homomorphic Coordinates of Dempster’s Semigroup

Coordinates of belief functions on two-element frame of discernment are defined using homomorphisms of Dempster’s semigroup (the algebra of belief functions with Dempster’s rule). Three systems of the ...

Daniel, Milan
Ústav informatiky, 2015

The Distribution Mapping Functions
Jiřina, Marcel
2015 - English The target of this study is to make clear the difference of the distribution mapping function introduced in 2003 and the classical notion of point processes theory, the counting function N, Ripley’s K-function, and other two distance functions, F and G-functions. We summarize here necessary starting points from the point process theory using the famous work by Baddeley, a two-volume book of Daley and Vere-Jones and short paper by Dixon. When dealing with the distribution mapping function we use up-to-date formulations used in various papers since 2003. Keywords: multidimensional data; correlation dimension; distance; metrics,; distribution mapping function Available in a digital repository NRGL The Distribution Mapping Functions

The target of this study is to make clear the difference of the distribution mapping function introduced in 2003 and the classical notion of point processes theory, the counting function N, Ripley’s ...

Jiřina, Marcel
Ústav informatiky, 2015

A New Proof of the Hansen-Bliek-Rohn Optimality Result
Rohn, Jiří
2014 - English Keywords: interval linear equations; unit midpoint; Hansen-Bliek-Rohn optimality result Available in a digital repository NRGL A New Proof of the Hansen-Bliek-Rohn Optimality Result

Rohn, Jiří
Ústav informatiky, 2014

Fast nondeterministic matrix multiplication via derandomization of Freivalds' algorithm (Revised corrected version)
Wiedermann, Jiří
2014 - English Keywords: matrix multiplication; verification of matrix product; nondeterministic algorithms; modular arithmetic; complexity Available in a digital repository NRGL Fast nondeterministic matrix multiplication via derandomization of Freivalds' algorithm (Revised corrected version)

Wiedermann, Jiří
Ústav informatiky, 2014

Representations of Boolean Functions by Perceptron Networks
Kůrková, Věra
2014 - English Limitations of capabilities of shallow perceptron networks are investigated. Lower bounds are derived for growth of numbers of units and sizes of output weights in networks representing Boolean functions of d variables. It is shown that for large d, almost any randomly chosen Boolean function cannot be tractably represented by shallow perceptron networks, i.e., each its representation requires a network with number of units or sizes of output weights depending on d exponentially Keywords: perceptron networks; model complexity; Boolean functions Available in digital repository of the ASCR Representations of Boolean Functions by Perceptron Networks

Limitations of capabilities of shallow perceptron networks are investigated. Lower bounds are derived for growth of numbers of units and sizes of output weights in networks representing Boolean ...

Kůrková, Věra
Ústav informatiky, 2014

Noise revealing in Golub-Kahan bidiagonalization as a mean of regularization in discrete inverse problems
Kubínová, Marie; Hnětynková, Iveta
2014 - English Keywords: ill-posed problems; regularization; Krylov subspace Available in a digital repository NRGL Noise revealing in Golub-Kahan bidiagonalization as a mean of regularization in discrete inverse problems

Kubínová, Marie; Hnětynková, Iveta
Ústav informatiky, 2014

On three equivalent methods for parameter estimation problem based on spatio-temporal FRAP data
Matonoha, Ctirad; Papáček, Š.
2014 - English Keywords: inverse problem formulation; Tikhonov regularizaton; least-squares problem Available in a digital repository NRGL On three equivalent methods for parameter estimation problem based on spatio-temporal FRAP data

Matonoha, Ctirad; Papáček, Š.
Ústav informatiky, 2014

Explaining Anomalies with Sapling Random Forests
Pevný, T.; Kopp, Martin
2014 - English The main objective of anomaly detection algorithms is finding samples deviating from the majority. Although a vast number of algorithms designed for this already exist, almost none of them explain, why a particular sample was labelled as an anomaly. To address this issue, we propose an algorithm called Explainer, which returns the explanation of sample’s differentness in disjunctive normal form (DNF), which is easy to understand by humans. Since Explainer treats anomaly detection algorithms as black-boxes, it can be applied in many domains to simplify investigation of anomalies. The core of Explainer is a set of specifically trained trees, which we call sapling random forests. Since their training is fast and memory efficient, the whole algorithm is lightweight and applicable to large databases, datastreams, and real-time problems. The correctness of Explainer is demonstrated on a wide range of synthetic and real world datasets. Keywords: anomaly explanation; decision trees; feature selection; random forest Available in digital repository of the ASCR Explaining Anomalies with Sapling Random Forests

The main objective of anomaly detection algorithms is finding samples deviating from the majority. Although a vast number of algorithms designed for this already exist, almost none of them explain, ...

Pevný, T.; Kopp, Martin
Ústav informatiky, 2014

Case Study in Approaches to the Classification of Audiovisual Recordings of Lectures and Conferences
Pulc, P.; Holeňa, Martin
2014 - English Several methods for classification of semistructured documents already exist, thus also classifications for individual modalities of multimedia content. However, every classifier can behave differently on different data modalities and can be differently appropriate for classification of the considered multimedia content as a whole. Because of that, relying on a single classifier or a static weighting of the classification of individual modalities is not adequate. The present paper describes a case study in searching for suitable classification methods, and in investigating appropriate methods for the aggregation of their results to determine a final class of a lecture or conference recording. Keywords: multimedial data; classification; ensembles of classifiers Available in digital repository of the ASCR Case Study in Approaches to the Classification of Audiovisual Recordings of Lectures and Conferences

Several methods for classification of semistructured documents already exist, thus also classifications for individual modalities of multimedia content. However, every classifier can behave ...

Pulc, P.; Holeňa, Martin
Ústav informatiky, 2014

Meta-Parameters of Kernel Methods and Their Optimization
Vidnerová, Petra; Neruda, Roman
2014 - English In this work we deal with the problem of metalearning for kernel based methods. Among the kernel methods we focus on the support vector machine (SVM), that have become a method of choice in a wide range of practical applications, and on the regularization network (RN) with a sound background in approximation theory. We discuss the role of kernel function in learning, and we explain several search methods for kernel function optimization, including grid search, genetic search and simulated annealing. The proposed methodology is demonstrated on experiments using benchmark data sets. Keywords: kernel methods; metalearning; computational intelligence Available in digital repository of the ASCR Meta-Parameters of Kernel Methods and Their Optimization

In this work we deal with the problem of metalearning for kernel based methods. Among the kernel methods we focus on the support vector machine (SVM), that have become a method of choice in a wide ...

Vidnerová, Petra; Neruda, Roman
Ústav informatiky, 2014