By Hendrik Blockeel, Matthijs van Leeuwen, Veronica Vinciotti
This ebook constitutes the refereed convention lawsuits of the thirteenth foreign 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 every kind of modeling and research equipment, regardless of self-discipline. The papers disguise all facets of clever facts research, together with papers on clever help for modeling and interpreting facts from advanced, dynamical systems.
Read Online or Download Advances in Intelligent Data Analysis XIII: 13th International Symposium, IDA 2014, Leuven, Belgium, October 30 – November 1, 2014. Proceedings PDF
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Additional info for Advances in Intelligent Data Analysis XIII: 13th International Symposium, IDA 2014, Leuven, Belgium, October 30 – November 1, 2014. Proceedings
A Seeded Search for the Modularisation of Sequential Software Versions. Journal of Object Technology 11(2), 6:1–6:27 4. : An Application of Intelligent Data Analysis Techniques to a Large Software Engineering Dataset. -F. ) IDA 2009. LNCS, vol. 5772, pp. 261–272. Springer, Heidelberg (2009) 5. : Clustering sparse graphs. Advances in Neural Information Processing Systems (2012) 6. : A metrics suite for object oriented design. IEEE Trans, Software Eng. 20(6), 476–493 (1994) 7. : Structured Design.
6 A State Space Representation of the OU(p) Process The decomposition of the OU(p) process xκ,σ (t) as a linear combination of simpler processes of order 1 (Thm. 1), leads to an expression of the process by means of a state space model. This provides a uniﬁed approach for computing the likelihood of xκ,σ (t) through a Kalman ﬁlter. Moreover, it can be used to show that xκ,σ (t) is an ARMA(p, p − 1) whose coeﬃcients can be computed from κ. In order to ease notation, we consider that the components of κ are all diﬀerent.
Section 4 describes the simple, yet eﬀective method with which we estimate a pattern spectrum from the original data. In Section 5 we report experiments on artiﬁcially generated data sets and thus demonstrate the quality of pattern spectrum estimation. Finally, in Section 6 we draw conclusions from our discussion. 1 2 Even though pattern spectrum ﬁltering was presented for time-binned data in  (which reduces the problem to classical frequent item set mining: each time bin gives rise to one transaction), it can easily be transferred to the continuous domain.