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

Eulerovské chemické transportní modely, jejich výhody a možnosti využití
Resler, Jaroslav; Karel, J.; Jireš, R.; Liczki, Jitka; Belda, Michal; Eben, Kryštof; Kasanický, Ivan; Juruš, Pavel; Vlček, O.; Benešová, N.; Kazmuková, M.
2014 - Czech Available on request at various institutes of the ASCR Eulerovské chemické transportní modely, jejich výhody a možnosti využití

Resler, Jaroslav; Karel, J.; Jireš, R.; Liczki, Jitka; Belda, Michal; Eben, Kryštof; Kasanický, Ivan; Juruš, Pavel; Vlček, O.; Benešová, N.; Kazmuková, M.
Ústav informatiky, 2014

Robust Regularized Cluster Analysis for High-Dimensional Data
Kalina, Jan; Vlčková, Katarína
2014 - English This paper presents new approaches to the hierarchical agglomerative cluster analysis for high-dimensional data. First, we propose a regularized version of the hierarchical cluster analysis for categorical data with a large number of categories. It exploits a regularized version of various test statistics of homogeneity in contingency tables as the measure of distance between two clusters. Further, our aim is cluster analysis of continuous data with a large number of variables. Various regularization techniques tailor-made for high-dimensional data have been proposed, which have however turned out to suffer from a high sensitivity to the presence of outlying measurements in the data. As a robust solution, we recommend to combine two newly proposed methods, namely a regularized version of robust principal component analysis and a regularized Mahalanobis distance, which is based on an asymptotically optimal regularization of the covariance matrix. We bring arguments in favor of the newly proposed methods. Keywords: cluster analysis; robust data mining; big data; regularization Available at various institutes of the ASCR Robust Regularized Cluster Analysis for High-Dimensional Data

This paper presents new approaches to the hierarchical agglomerative cluster analysis for high-dimensional data. First, we propose a regularized version of the hierarchical cluster analysis for ...

Kalina, Jan; Vlčková, Katarína
Ústav informatiky, 2014

Important Markov-Chain Properties of (1,lambda)-ES Linear Optimization Models
Chotard, A.; Holeňa, Martin
2014 - English Several recent publications investigated Markov-chain modelling of linear optimization by a (1,lambda)-ES, considering both unconstrained and linearly constrained optimization, and both constant and varying step size. All of them assume normality of the involved random steps. This is a very strong and specific assumption. The objective of our contribution is to show that in the constant step size case, valuable properties of the Markov chain can be obtained even for steps with substantially more general distributions. Several results that have been previously proved using the normality assumption are proved here in a more general way without that assumption. Finally, the decomposition of a multidimensional distribution into its marginals and the copula combining them is applied to the new distributional assumptions, particular attention being paid to distributions with Archimedean copulas. Keywords: evolution strategies; random steps; linear optimization; Markov chain models; Archimedean copulas Available in digital repository of the ASCR Important Markov-Chain Properties of (1,lambda)-ES Linear Optimization Models

Several recent publications investigated Markov-chain modelling of linear optimization by a (1,lambda)-ES, considering both unconstrained and linearly constrained optimization, and both constant and ...

Chotard, A.; Holeňa, Martin
Ústav informatiky, 2014

Robustness of High-Dimensional Data Mining
Kalina, Jan; Duintjer Tebbens, Jurjen; Schlenker, Anna
2014 - English Standard data mining procedures are sensitive to the presence of outlying measurements in the data. This work has the aim to propose robust versions of some existing data mining procedures, i.e. methods resistant to outliers. In the area of classification analysis, we propose a new robust method based on a regularized version of the minimum weighted covariance determinant estimator. The method is suitable for data with the number of variables exceeding the number of observations. The method is based on implicit weights assigned to individual observations. Our approach is a unique attempt to combine regularization and high robustness, allowing to downweight outlying high-dimensional observations. Classification performance of new methods and some ideas concerning classification analysis of high-dimensional data are illustrated on real raw data as well as on data contaminated by severe outliers. Keywords: classification analysis; robust estimation; high-dimensional data Available in digital repository of the ASCR Robustness of High-Dimensional Data Mining

Standard data mining procedures are sensitive to the presence of outlying measurements in the data. This work has the aim to propose robust versions of some existing data mining procedures, i.e. ...

Kalina, Jan; Duintjer Tebbens, Jurjen; Schlenker, Anna
Ústav informatiky, 2014

A posteriori algebraic error estimation in numerical solution of linear diffusion PDEs
Papež, Jan; Vohralík, M.
2014 - English Keywords: finite element method; algebraic error; a posteriori error estimation; stopping criteria Available in a digital repository NRGL A posteriori algebraic error estimation in numerical solution of linear diffusion PDEs

Papež, Jan; Vohralík, M.
Ústav informatiky, 2014

Towards Low-Dimensional Gaussian Process Metamodels for CMA-ES
Bajer, Lukáš; Holeňa, Martin
2014 - English Gaussian processes and kriging models has attracted attention of researchers from different areas of black-box optimization, especially since Jones’ introduction of the Efficient Global Optimization (EGO) algorithm. However, current implementations of the EGO or real-world applications are rather few. We conjecture that the EGO is not suitable for higher-dimensional optimization and try to investigate whether hybridization of a low-dimensional local optimization with the current state-of-the-art continuous black-box optimizer CMA-ES (Covariance Matrix Adaptation Evolution Strategy) could help. In this paper, only a first proposal of such a GP/CMA-ES connection is described and some preliminary tests are presented. Keywords: CMA-ES; Gaussian processes; global optimization; surrogate model; metamodel Available in digital repository of the ASCR Towards Low-Dimensional Gaussian Process Metamodels for CMA-ES

Gaussian processes and kriging models has attracted attention of researchers from different areas of black-box optimization, especially since Jones’ introduction of the Efficient Global Optimization ...

Bajer, Lukáš; Holeňa, Martin
Ústav informatiky, 2014

Interpreting and Clustering Outliers with Sapling Random Forests
Kopp, Martin; Pevný, T.; Holeňa, Martin
2014 - English The main objective of outlier detection is finding samples considerably deviating from the majority. Such outliers, often referred to as anomalies, are nowadays more and more important, because they help to uncover interesting events within data. Consequently, a considerable amount of statistical and data mining techniques to identify anomalies was proposed in the last few years, but only a few works at least mentioned why some sample was labelled as an anomaly. Therefore, we propose a method based on specifically trained decision trees, called sapling random forest. Our method is able to interpret the output of arbitrary anomaly detector. The explanation is given as a subset of features, in which the sample is most deviating, or as conjunctions of atomic conditions, which can be viewed as antecedents of logical rules easily understandable by humans. To simplify the investigation of suspicious samples even more, we propose two methods of clustering anomalies into groups. Such clusters can be investigated at once saving time and human efforts. The feasibility of our approach is demonstrated on several synthetic and one real world datasets. Keywords: anomaly detection; anomaly interpretation; clustering; decision trees; feature selection; random forest Available in digital repository of the ASCR Interpreting and Clustering Outliers with Sapling Random Forests

The main objective of outlier detection is finding samples considerably deviating from the majority. Such outliers, often referred to as anomalies, are nowadays more and more important, because they ...

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

Online System for Fire Danger Rating in Colorado
Vejmelka, Martin; Kochanski, A.; Mandel, J.
2014 - English A method for the data assimilation of fuel moisture surface observations has been developed for the purpose of incorporation in wildfire forecasting and fire danger rating. In this work, we describe the method itself and also an online computer system that implements the method and combines it with the Real-Time Mesoscale Analysis to track local weather conditions and estimate the fuel moisture content in the state of Colorado. We discuss the construction of the system and future development. Keywords: fire danger; fuel moisture; data assimilation; remote automated weather stations; real-time mesoscale analysis; software; nebezpečí požáru; vlhkost paliva; asimilace dat; vzdálené automatické meteostanice Available in digital repository of the ASCR Online System for Fire Danger Rating in Colorado

A method for the data assimilation of fuel moisture surface observations has been developed for the purpose of incorporation in wildfire forecasting and fire danger rating. In this work, we describe ...

Vejmelka, Martin; Kochanski, A.; Mandel, J.
Ústav informatiky, 2014

On the Consistency of an Estimator for Hierarchical Archimedean Copulas
Górecki, J.; Hofert, M.; Holeňa, Martin
2014 - English The paper addresses an estimation procedure for hierarchical Archimedean copulas, which has been proposed in the literature. It is shown here that this estimation is not consistent in general. Furthermore, a correction is proposed, which leads to a consistent estimator. Keywords: hierarchical Archimedean copula; Kendall distribution function; parameter estimation; structure determination; consistency Available on request at various institutes of the ASCR On the Consistency of an Estimator for Hierarchical Archimedean Copulas

The paper addresses an estimation procedure for hierarchical Archimedean copulas, which has been proposed in the literature. It is shown here that this estimation is not consistent in general. ...

Górecki, J.; Hofert, M.; Holeňa, Martin
Ústav informatiky, 2014