Adaptive and Natural Computing Algorithms: 10th by Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič,

By Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)

The two-volume set LNCS 6593 and 6594 constitutes the refereed court cases of the tenth overseas convention on Adaptive and ordinary Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers awarded have been rigorously reviewed and chosen from a complete of a hundred and forty four submissions. the second one quantity comprises forty-one papers geared up in topical sections on trend reputation and studying, delicate computing, platforms conception, help vector machines, and bioinformatics.

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Extra info for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II

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N} already identified as a steady-state. By definition, |yi − yj | ≤ ∀i, j = 1, . . , n and i = j, where > 0 is the defined tolerance. Let yM = max {yi } and ym = min {yi } , ∀i = l, . . , n be the maximum and minimum values, respectively, for Y, and that yr (r = n + 1) is the next sample value. Next we prove that yr maintains the stable behavior of Y only for a limited range of values. Theorem 1. , |yi − yj | ≤ , ∀i, j = 1, . . , n + 1 . Proof. In fact, if ym ≤ yr ≤ yM , then |yi − yr| ≤ |ym − yM | ≤ , for all i = 1, .

The concept was independently introduced by Hart [1] (then working at the Electric Power Research Institute) and by Sultamen (Electricité de France) [2]. Over the last decades, due to the pressing environmental and economic issues, the interest in this area has increased, being the focus of PhD theses as [3]. In 1996, the first NILM system was commercialized by the company Enetics, Inc.. The main steps in a non-intrusive load monitoring system are: a) the acquisition of electrical signals, b) extraction of the important events and/or characteristics and c) production of a classifier of electrical events (see Figure 1).

Keywords: Neural networks, SVM, prediction, transparency. 1 Introduction Explaining prediction models and their predictions is an integral part of machine learning. The purpose of such methods is making models more informative, easier to understand, and easier to use. These benefits are especially welcome when using non-transparent prediction models, such as artificial neural networks and SVM. Some of the most popular learning algorithms (naive Bayes, decision trees) owe a part of their popularity to their ability to produce models which are inherently easy to interpret.

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