Titlebook: Discovery Science; 14th International C Tapio Elomaa,Jaakko Hollmén,Heikki Mannila Conference proceedings 2011 Springer-Verlag GmbH Berlin

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0302-9743 ubmissions. The papers cover a wide range including the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their application to knowledge discovery.978-3-642-24476-6978-3-642-24477-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Income Distribution and Economic Growthsuch as multi-label classification, hierarchical classification or ordinal classification, may be addressed within the framework of learning from label preferences. We also briefly address theoretical questions as well as algorithmic and complexity issues.

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Learning from Label Preferences,such as multi-label classification, hierarchical classification or ordinal classification, may be addressed within the framework of learning from label preferences. We also briefly address theoretical questions as well as algorithmic and complexity issues.

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Information Distance and Its Extensions,ry has found many applications. Recently we have introduced two extensions to this theory concerning multiple objects and irrelevant information. This expository article will focus on explaining the main ideas behind this theory, especially these recent extensions, and their applications. We will also discuss some very preliminary applications.

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A Methodology for Mining Document-Enriched Heterogeneous Information Networks,ed. We exploit this feature vector construction process to devise an efficient classification algorithm. We demonstrate the approach by applying it to the task of categorizing video lectures. We show that our approach exhibits low time and space complexity without compromising classification accuracy.

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