Titlebook: Discovery Science; 19th International C Toon Calders,Michelangelo Ceci,Donato Malerba Conference proceedings 2016 Springer International Pu

Gourmet

柏树

HAVOC

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Exceptional Preferences Miningal pairwise label ranking behavior. As proof of concept, we explore five datasets. The results confirm that the new task EPM can deliver interesting knowledge. The results also illustrate how the visualization of the preferences in a Preference Matrix can aid in interpreting exceptional preference subgroups.

BUMP

Local Subgroup Discovery for Eliciting and Understanding New Structure-Odor Relationshipsewed distributions, our approach extracts the top-. unredundant subgroups interpreted as descriptive rules .. Our experiments on benchmark and olfaction datasets demonstrate the capabilities of our approach with direct applications for the perfume and flavor industries.

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Atheroma

Conference proceedings 2016he 30 full papers presented together with 5 abstracts of invited talks in this volume were carefullyreviewed and selected from 60 submissions.The conference focuses on following topics: Advances in the development and analysis of methods for discovering scientific knowledge, coming from machine learn

鲁莽

Predicting Wildfiresdividually and combined together. We successfully use under-sampling to deal with the high skew in the data set. We find that combining the approaches significantly improves the similar results obtained by each method individually.

enmesh

0302-9743 entific knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scientific domains..978-3-319-46306-3978-3-319-46307-0Series ISSN 0302-9743 Series E-ISSN 1611-3349