Data Analysis, Machine Learning and Knowledge Discovery
Myra Spiliopoulou, Lars Schmidt-Thieme, Ruth Janning
Springer Science & Business Media, Nov 26, 2013 - Computers - 470 pages
Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012.
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accuracy aggregation algorithm ANOVA applied approach audio binary bootstrap calculated coefficients compared Computational conjoint analysis covariance Data Analysis data set data stream database defined depth distance donor limit Dortmund error estimation evaluation extreme returns Gaussian Germany e-mail imputation International Publishing Switzerland Isomap Journal judgment rule Knowledge Discovery Knowledge Organization label Learning and Knowledge linear loss function Machine Learning matrix measures missing values multinomial multivariate Music Information Retrieval normal distribution number of clusters objects observations OECD onset detection ontology optimization outliers paper parameter performance PISA pre-processing prediction problem procedure Publishing Switzerland 2014 query Rand index random random forest rank samples scale Sect segments selection shows simulated spectral clustering Spiliopoulou Springer International Publishing statistical structure Studies in Classification support vector Support Vector Machines symbolic data Table techniques training data validity variables variance