Titlebook: Data Analysis, Machine Learning and Knowledge Discovery; Myra Spiliopoulou,Lars Schmidt-Thieme,Ruth Janning Conference proceedings 2014 Sp

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书目名称Data Analysis, Machine Learning and Knowledge Discovery网络公开度学科排名

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书目名称Data Analysis, Machine Learning and Knowledge Discovery被引频次学科排名

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书目名称Data Analysis, Machine Learning and Knowledge Discovery年度引用学科排名

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The Most Dangerous Districts of Dortmunde this risk the offences reported by police press reports in the year 2011 (Presseportal, ., 2011) were analyzed and weighted by their maximum penalty corresponding to the German criminal code. The resulting danger index was used to rank the districts. Moreover, the socio-demographic influences on t

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How Many Bee Species? A Case Study in Determining the Number of Clustersmethods of estimating . ignore this. . can be determined by listing a number of requirements for a good clustering in the given application and finding a . that fulfils them all. The approach is illustrated by application to the problem of finding the number of species in a data set of Australasian

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Two-Step Linear Discriminant Analysis for Classification of EEG Dataensional spatio-temporal data with separable covariance matrix. At first all features are divided into subgroups and linear discriminant analysis (LDA) is used to obtain a score for each subgroup. Then LDA is applied to these scores, producing the overall score used for classification. In this way w

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Predictive Validity of Tracking Decisions: Application of a New Validation Criterionbetween school tracks. However, the correctness of tracking decisions usually is based on whether or not a student has kept the track she or he was initially assigned to. To overcome the neglect of misclassified students, we propose an alternative validation criterion for tracking decisions. We appl

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The Alpha-Procedure: A Nonparametric Invariant Method for Automatic Classification of Multi-Dimensioon of two learning classes in a proper multi-dimensional rectifying feature space and the stepwise construction of a separating hyperplane in that space. The dimension of the space, i.e. the number of features that is necessary for a successful classification, is determined step by step using two-di

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Support Vector Machines on Large Data Sets: Simple Parallel Approachesl cost due to the cubic runtime complexity is problematic for larger data sets. To mitigate this, Graf et al. (Adv. Neural Inf. Process. Syst. 17:521–528, 2005) proposed the Cascade SVM. It is a simple, stepwise procedure, in which the SVM is iteratively trained on subsets of the original data set a

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Titlebook: Data Analysis, Machine Learning and Knowledge Discovery; Myra Spiliopoulou,Lars Schmidt-Thieme,Ruth Janning Conference proceedings 2014 Sp

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