Titlebook: New Developments in Classification and Data Analysis; Proceedings of the M H.-H. Bock,W. Gaul,Angela Montanari Conference proceedings 2005

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窗簾等

Patrick J.F. Groenen,Patrizia Giaquinto,Henk A.L. Kiersr physics, the domain language is visible as such, the notions used in the interdisciplinary field of artificial intelligence (AI) already seem like everyday language and therefore hide their inner complexity. AI vocabulary comprises many anthropomorphisms to describe seemingly cognitive-like comput

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Patrick J.F. Groenen,Alex J. Koningsical capabilities of human beings beyond their current limiThis book examines the contributions of the transhumanism approach to technology, in particular the contributed chapters are wary of the implications of this popular idea..The volume is organized into four parts concerning philosophical, mi

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Methods to Compare Nonparametric Classifiers and to Select the Predictorsthe importance, in nonparametric models, to eliminate the noise variables with a simple selection procedure. It is shown that a simpler model usually gives lower prediction error and is more interpretable. We show some empirical results applying nonparametric classification models on real and artifi

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Variable Selection in Cell Classification Problems: A Strategy Based on Independent Component Analysrge number of variables (genes), relative to the number of observations (cells). This condition makes most of the standard statistical methods for classification difficult to employ. The proposed solution consists of building classification rules on subsets of genes showing a behavior across the cel

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Simplifying Classification Trees Through Consensus Methodsused to analyse a set of trees obtained from independent data sets or from resampling methods like bootstrap or cross validation applied to the same training sample. In this paper we consider, as an alternative to the pruning techniques, a modified version of a consensus algorithm we have previously

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Selecting the Training Set in Classification Problems with Rare Eventsof the rare class when forming the training set. For some classification algorithms, like logistic classification, there are theoretical results that justify such an approach. Similar results are not available for other popular classification algorithms like classification trees. In this paper the u

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A Classification and Discrimination Integrated Strategy Conducted on Symbolic Data for Missing Data e analysis results. In this paper we propose a strategy based on Classification and Discrimination methods conducted on symbolic data and it enables us to extract both compatibility rules and to impute data in order to reconstruct the information. The strategy makes use of tools developed in statist

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