Advances in Intelligent Data Analysis XI: 11th International by Gavin C. Cawley (auth.), Jaakko Hollmén, Frank Klawonn,

By Gavin C. Cawley (auth.), Jaakko Hollmén, Frank Klawonn, Allan Tucker (eds.)

This booklet constitutes the refereed complaints of the eleventh overseas convention on clever information research, IDA 2012, held in Helsinki, Finland, in October 2012. The 32 revised complete papers awarded including three invited papers have been conscientiously reviewed and chosen from 88 submissions. All present features of clever facts research are addressed, together with clever aid for modeling and reading facts from advanced, dynamical platforms. The papers concentrate on novel functions of IDA options to, e.g., networked electronic details structures; novel modes of information acquisition and the linked matters; robustness and scalability problems with clever info research strategies; and visualization and dissemination results.

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With regard to the latter, usage of parallel compute engines has not gained much attention as the compute time tends to be less critical – especially in times when computational resources even on desktop computers are available in such abundance. Nevertheless many if not all relevant algorithms rely on heuristics or user supplied parameters to somewhat reduce the otherwise entirely infeasible hypothesis space. In this paper, we argue that modern architectures, which provide access to numerous parallel computing resources, emphasized by the recent advance of multi-core architectures, can also be utilized to reduce the effect of these user parameters or other algorithmic heuristics.

9–11] in which, in (2), p is used as an exponent of the weights, but the distance is Euclidean; (iii) MPAM: PAM using the Minkowski metric with an arbitrary p; (iv) MW-PAM: our Minkowski weighted PAM; (v)Build + PAM: PAM initialized with the medoids generated by Build; (vi)Build + WPAM: WPAM also initialized with Build; (vii)M Build + MPAM: MPAM initialized using the Minkowski dissimilarity-based Build; (viii)M Build + MW-PAM: MW-PAM also initialized using the Minkowski dissimilarity-based Build.

Many of these algorithms (and these are the ones we are interested in here) follow some iterative scheme, where at each iteration a given model is refined. g. the neural network after one more gradient descent step or after another branch has been added to the decision tree. The two functions s(·) and r(·) describe a selection resp. refinement operation. In the case of the neural network, the refinement operator represents the gradient descent step and there is no real selection operator. For the decision tree, the refinement operator would actually return a set of expanded decision trees and the selection operator picks the one with the highest information gain.

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