By Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)
The two-volume set LNCS 6593 and 6594 constitutes the refereed complaints of the tenth overseas convention on Adaptive and normal Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers awarded have been conscientiously reviewed and chosen from a complete of a hundred and forty four submissions. the second one quantity contains forty-one papers prepared in topical sections on trend popularity and studying, smooth computing, platforms conception, aid vector machines, and bioinformatics.
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Additional info for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II
694–709. Springer, Heidelberg (2009) An Experimental Study on Electrical Signature Identification of Non-Intrusive Load Monitoring (NILM) Systems Marisa B. pt Abstract. Electrical load disambiguation for end-use recognition in the residential sector has become an area of study of its own right. g. the electrical service entrance for the house) using a non-intrusive load monitoring (NILM) approach. This work presents the development of an algorithm for electrical feature extraction and pattern recognition, capable of determining the individual consumption of each device from the aggregate electric signal of the home.
D|| is lower than parameter α, they are merged into a single point with mass equal to unity. fi/somtoolbox/. Gravitational Clustering of the SOM 15 aﬀect smaller or less dense ones. The experiments presented in Section 3 prove that such approach is beneﬁcial. It is obvious that the number of points decreases during iterations, when the appropriate G is chosen. At each iteration of an algorithm, every point x in the remaining set of points denoted with P is considered once. Then we have to choose another point y and move both of them according to Eq.
Data Mining: Introductory and Advanced Topics. Prentice Hall, Englewood Cliﬀs (2003) 2. : Data mining: practical machine learning tools and techniques. Elsevier, Amsterdam (2005) 3. : Self-organizing maps. Springer, Heidelberg (2001) 4. : Clustering of the Self-Organizing Map. IEEE Trans. on Neural Networks 11(3), 586–600 (2000) 5. : Emergence in Self Organizing Feature Maps. In: 6th International Workshop on Self-Organizing Maps (2007) 6. : Automatic Cluster Detection in Kohonen’s SOM. IEEE Trans.