By Jaakko Hollmén, Jarkko Tikka (auth.), Michael R. Berthold, John Shawe-Taylor, Nada Lavrač (eds.)

Weareproudtopresenttheproceedingsoftheseventhbiennialconferenceinthe clever information research sequence. The convention came about in Ljubljana, Slo- nia, September 6-8, 2007. IDA keeps to extend its scope, caliber and measurement. It begun as a small side-symposium as a part of a bigger convention in 1995 in Baden-Baden(Germany).It quick attractedmoreinterest in either submissions and attendance because it moved to London (1997) after which Amsterdam (1999). the following 3 conferences have been held in Lisbon (2001), Berlin (2003) after which Madrid in 2005. The enhancing caliber of the submissions has enabled the organizers to gather courses of ever-increasing consistency and caliber. This 12 months we madea rigorousselectionof33papersoutofalmost100submissions.Theresu- ing oral displays have been then scheduled in a single-track, two-and-a-half-day convention application, summarized within the e-book that you've prior to you. based on the said IDA objective of “bringing jointly researchers from diversified disciplines,” we think we now have completed a good stability of presentationsfromthemoretheoretical–bothstatisticalandmachinelearning– to the extra application-oriented components that illustrate how those thoughts can beusedinpractice.Forexample,theproceedingsincludepaperswiththeoretical contributions facing statistical methods to series alignment in addition to papers addressing functional difficulties within the parts of textual content classi?cation and scientific facts research. it's reassuring to work out that IDA maintains to carry such assorted components jointly, therefore assisting to cross-fertilize those ?elds.

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Additional resources for Advances in Intelligent Data Analysis VII: 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007. Proceedings

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1 Introduction We consider the problem of learning to align biological sequences given a training set of correct global alignments. Learning to align means learning the alignment parameters (the scoring matrix and gap costs) in such a way that the correct alignment has the best score among all possible alignments between two given sequences. This task is known as the Inverse Parametric Sequence Alignment Problem (IPSAP) introduced by Gusfield [4] and falls in the category of inverse parametric optimization problems [2].

Every symbol s1 (i) corresponds to a specific symbol s2 (i). If these symbols are equal, we call this a match. If they are not equal, this is a mismatch. If one of the symbols is a −, this is called an indel or a gap. With each possible match, substitution or gap at position i, a score is attached. To quantify these scores, three score parameters can be used, one corresponding to a match (αm ), one to a mismatch (αs ), and one to a gap (αg ). The score of the global alignment can be expressed as a linear function of these parameters: φ (S1 , S2 , A) = αm m + αs s + αg g = αT x where we have defined the vectors αT = [αm αs αg ] and xT = [m s g] and m, s and g represent the number of matches, mismatches and gaps in the alignment.

3 Experimental Results We experimented with the multiplicative updates for NQP to investigate their performance on problems in L1 –regularized linear regression. For these experiments, we created artificial data sets {(xα , yα )}nα=1 with inputs of varying dimensionality d ∼ 102−3 . Each data set was created as follows. First, we randomly generated a “ground truth” weight vector w ∈ d with precisely d/3 negative elements, d/3 zero elements, and d/3 positive elements. The magnitudes of nonzero elements in this weight vector were sampled uniformly from the unit interval.

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