Shortcuts
 
PageMenu- Hauptmenü-
Page content

Kategorienanzeige

MAB

Combining Soft Computing and Statistical Methods in Data Analysis
Kategorie Beschreibung
036aXA-DE‡XD-US
037beng
077a338735275 Buchausg. u.d.T.: ‡Combining soft computing and statistical methods in data analysis
087q978-3-642-14745-6
100 Borgelt, Christian
104bGonzález-Rodríguez, Gil
108bTrutschnig, Wolfgang
112bLubiano, María Asunción
116bGil, Mariá Angeles
120bGrzegorzewski, Przemysław
124bHryniewicz, Olgierd
331 Combining Soft Computing and Statistical Methods in Data Analysis
410 Berlin, Heidelberg
412 Springer Berlin Heidelberg
425 2010
425a2010
433 Online-Ressource (630p. 96 illus, digital)
451 Advances in Intelligent and Soft Computing ; 77
501 Includes bibliographical references and index
517 Title Page; Preface; Members of Committees; Contents; Prior Knowledge in the Classification of Biomedical Data; Estimation of a Simple Genetic Algorithm Applied to a Laboratory Experiment; A Comparison of Robust Methods for Pareto Tail Modeling in the Case of Laeken Indicators; R Code for Hausdorff and Simplex Dispersion Orderings in the 2D Case; On Some Confidence Regions to Estimate a Linear Regression Model for Interval Data; Possibilistic Coding: Error Detection vs. Error Correction; Coherent Correction for Conditional Probability Assessments with R; Inferential Rules for Weak Graphoid. Fast Factorization of Probability Trees and Its Application to Recursive Trees LearningOption Pricing in Incomplete Markets Based on Partial Information; Lorenz Curves of extrema; Likelihood in a Possibilistic and Probabilistic Context: A Comparison; Nonparametric Predictive Inference for Order Statistics of Future Observations; Expected Pair-Wise Comparison of the Outcomes of a Fuzzy Random Variable; The Behavioral Meaning of the Median; Functional Classification and the Random Tukey Depth. Practical Issues; On Concordance Measures and Copulas with Fractal Support. Factorisation Properties of the Strong ProductHadamard Majorants for the Convex Order and Applications; How to Avoid LEM Cycles in Mutual Rank Probability Relations; Functional Inequalities Characterizing the Frank Family of Copulas; Recent Developments in Censored, Non-Markov Multi-State Models; Maximum Likelihood from Evidential Data: An Extension of the EM Algorithm; A Decision Rule for Imprecise Probabilities Based on Pair-Wise Comparison of Expectation Bounds; Handling Bipolar Knowledge with Credal Sets. Coherent Upper Conditional Previsions and Their Integral Representation with Respect to Hausdorff Outer MeasuresStatistical Inference with Belief Functions and Possibility Measures: A Discussion of Basic Assumptions; Representation of Exchangeable Sequences by Means of Copulas; Area-Level Time Models for Small Area Estimation of Poverty Indicators; Flood Analysis: On the Automation of the Geomorphological-Historical Method; Geometric Sampling: An Approach to Uncertainty in High Dimensional Spaces; Inverse Problems and Model Reduction Techniques. A Linearity Test for a Simple Regression Model with LR Fuzzy ResponseSoft Methods in Robust Statistics; S-Statistics and Their Basic Properties; Particle Swarm Optimization and Inverse Problems; Linear Approximations to the Power Function of Robust Tests; Decision Support for Evolving Clustering; On Jaffray's Decision Model for Belief Functions; Quasi Conjunction and p-Entailment in Nonmonotonic Reasoning; Elements of Robust Regression for Data with Absolute and Relative Information; On Testing Fuzzy Independence Application in Quality Control; The Fisher's Linear Discriminant. Testing Archimedeanity
527 Buchausg. u.d.T.: ‡Combining soft computing and statistical methods in data analysis
540aISBN 978-3-642-14746-3
700 |UYQ
700 |COM004000
700 |*62-07
700 |68U99
700 |62-06
700 |62-04
700 |00B25
700b|006.3
700b|519.5
700c|Q342
700g1271156261 SK 850
750 "Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a ""softening"" of classical methods, allowing, in particular, to work with imprecise or vague data, considering imprecise or generalized probabilities and fuzzy events, etc. About ten years ago the idea of establishing a recurrent forum for discussing new trends in the before-mentioned context was born and resulted in the first International Conference on Soft Methods in Probability and Statistics (SMPS) that was held in Warsaw in 2002. In the following years the conference took place in Oviedo (2004), in Bristol (2006) and in Toulouse (2008). In the current edition the conference returns to Oviedo. This edited volume is a collection of papers presented at the SMPS 2010 conference held in Mieres and Oviedo. It gives a comprehensive overview of current research into the fusion of soft methods with probability and statistics."
902s 209556331 Datenanalyse
902s 209152702 Wahrscheinlichkeitsrechnung
902s 209119799 Statistik
902s 212619101 Soft Computing
902f 00000057 Kongress
907s 209556331 Datenanalyse
907s 209152702 Wahrscheinlichkeitsrechnung
907s 209119799 Statistik
907s 212619101 Soft Computing
012 332859282
081 Borgelt, Christian <P>: Combining Soft Computing and Statistical Methods in Data Analysis
100 Springer E-Book
125aElektronischer Volltext - Campuslizenz
655e$uhttp://dx.doi.org/10.1007/978-3-642-14746-3
Schnellsuche