Download PDF by Pieter Abbeel (auth.), Peter A. Flach, Tijl De Bie, Nello: Machine Learning and Knowledge Discovery in Databases:

By Pieter Abbeel (auth.), Peter A. Flach, Tijl De Bie, Nello Cristianini (eds.)

ISBN-10: 3642334598

ISBN-13: 9783642334597

ISBN-10: 3642334601

ISBN-13: 9783642334603

This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed court cases of the eu convention on desktop studying and information Discovery in Databases: ECML PKDD 2012, held in Bristol, united kingdom, in September 2012. The a hundred and five revised examine papers offered including five invited talks have been rigorously reviewed and chosen from 443 submissions. the ultimate sections of the lawsuits are dedicated to Demo and Nectar papers. The Demo song contains 10 papers (from 19 submissions) and the Nectar song contains four papers (from 14 submissions). The papers grouped in topical sections on organization principles and widespread styles; Bayesian studying and graphical versions; type; dimensionality relief, function choice and extraction; distance-based equipment and kernels; ensemble equipment; graph and tree mining; large-scale, allotted and parallel mining and studying; multi-relational mining and studying; multi-task studying; traditional language processing; on-line studying and information streams; privateness and defense; scores and proposals; reinforcement studying and making plans; rule mining and subgroup discovery; semi-supervised and transductive studying; sensor facts; series and string mining; social community mining; spatial and geographical information mining; statistical tools and overview; time sequence and temporal information mining; and move learning.

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Extra info for Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I

Example text

Our bounds, which are linear in the VC-dimension associated with the dataset, are consistently smaller and less dependent on other parameters of the problem than Efficient Discovery of Association Rules and Frequent Itemsets through Sampling 29 Table 1. Required sample sizes (as number of transactions) as a function of the VC-dimension d, the maximum transaction size Δ, the number of items |I|, the accuracy ε, the failure probability δ, the minimum frequency θ, and the minimum confidence γ. Note that d ≤ Δ ≤ |I|.

Our approach is to ignore a large portion of suboptimal pairs, such that our search becomes linear. To this end, let p and n be two vectors, and let 1 ≤ b ≤ |p| be an integer. We say that a ≤ b is a head border of b if there are no integers i and j such that 1 ≤ i < a ≤ j ≤ b and fr (i, a − 1) ≥ fr (a, b). Similarly, we say that b ≥ a is a tail border of a if there are no indices a ≤ i ≤ b < j ≤ |p| such that fr (i, b) ≤ fr (b + 1, j). We denote the list of all head borders by bh(b, p, n) and the list of all tail borders by bt (a, p, n).

Choosing this parameter is difficult since there are no known bounds for the expected performance. Our approach for discovering optimal subtiles is greatly inspired by the work of Calders et al. [3] in which the goal was to compute the head frequency, hfr (i), given a stream of binary vectors. As Stijl is an iterative any-time algorithm, and hence iterative data mining approaches are related. The key idea of these approaches is to iteratively find the result providing the most novel information about the data with respect to what we already know [5, 11, 14].

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Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I by Pieter Abbeel (auth.), Peter A. Flach, Tijl De Bie, Nello Cristianini (eds.)


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