By Hendrik Blockeel, Matthijs van Leeuwen, Veronica Vinciotti
This publication constitutes the refereed convention lawsuits of the thirteenth overseas convention on clever information research, which used to be held in October/November 2014 in Leuven, Belgium. The 33 revised complete papers including three invited papers have been conscientiously reviewed and chosen from 70 submissions dealing with all types of modeling and research tools, regardless of self-discipline. The papers hide all points of clever info research, together with papers on clever help for modeling and reading information from complicated, dynamical systems.
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Extra info for Advances in Intelligent Data Analysis XIII: 13th International Symposium, IDA 2014, Leuven, Belgium, October 30 – November 1, 2014. Proceedings
Computer Science, Universitat Polit`ecnica de Catalunya, Barcelona, Spain 2 Dept. Matem` atiques, Universitat Aut` onoma de Barcelona, Spain 3 Depto. de M´etodos Matem´ atico-Cuantitativos, Universidad de la Rep´ ublica, Montevideo, Uruguay Abstract. The Ornstein-Uhlenbeck (OU) process is a well known continuous–time interpolation of the discrete–time autoregressive process of order one, the AR(1). We propose a generalization of the OU process that resembles the construction of autoregressive processes of higher order p > 1 from the AR(1).
It is a simple, easy to implement technique that has proven to be useful and robust in terms of modularisation . Algorithm 1. 2 Fitness Function A fitness function is used to measure the relative quality of the decomposed structure of system into subsystems (clusters). In our previous work, we experimented with two fitness functions: the Modularisation Quality (MQ) metric of Mancoridis et al , and the EValuation Metric (EVM) of Tucker et al . We also introduced EValuation Metric Difference (EVMD), a faster version of EVM.
Uhlenbeck  as a model for the velocities of a particle subject to the collisions with surrounding molecules. It is a well studied and accepted model for thermodynamics, chemical and other various stochastic processes found in physics and the natural sciences. g. where the underlying random noise is a L´evy process) as a model of ﬁnancial time series with applications to option pricing, portfolio optimization and risk theory, among others (see  and references therein). Supported by MICINN project TIN2011-27479-C04-03 (BASMATI), Gen.