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.
Metod potencialnych funkcij v teorii obucenia mashin (The method of potential
functions in machine learning theory) [in Russian]. Moscow: Nauka. Hastie, T.,
Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: Data
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European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings
The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
0 0.5 1 1.5 2 2.5 3 3.5 Online Q-learning vs Always On Q-learning (Online)
Always On 0 20 40 60 80 100 120 140 A v e r a g e d a i l y c o s t ( E u r o ) Time (
Days) Fig. 2. Online Q-learning vs “Always on” control Offline Q-learning Comfort
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