New PDF release: Advances in Knowledge Discovery and Data Mining: 5th

By Hosagrahar Visvesvaraya Jagadish⋆ (auth.), David Cheung, Graham J. Williams, Qing Li (eds.)

ISBN-10: 3540419101

ISBN-13: 9783540419105

This ebook constitutes the refereed complaints of the fifth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2001, held in Hong Kong, China in April 2001.
The 38 revised complete papers and 22 brief papers awarded have been conscientiously reviewed and chosen from a complete of 152 submissions. The publication bargains topical sections on net mining, textual content mining, purposes and instruments, suggestion hierarchies, characteristic choice, interestingness, series mining, spatial and temporal mining, organization mining, class and rule induction, clustering, and complex issues and new methods.

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Extra info for Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings

Sample text

The abstract class Aglet defines the fundamental methods that control the mobility and lifecycle of an aglet. It also provides access to the inherent attributes of an aglet, such as creation time, owner, codebase and trust level, as well as dynamic attributes, such as the arrival time at a site and address of the current context. The main function of the AgletProxy class is to provide a handle that is used to access the aglet. It also provides location transparency by forwarding requests to remote hosts and returning results to the local host.

The problem just described may readily be converted into one in which the threshold θ is taken to be zero. One does this by converting a data point x in the original space into x ˜ = [x, 1] in the enlarged space. Each hyperplane w in the original space with threshold θ can then be converted into [w, −θ] that passes through the origin in the enlarged space. Instead of searching for both an d-dimensional weight vector along with a threshold θ, we can search for an (d + 1)-dimensional weight vector along with an anticipated threshold of zero.

This ranked list is evaluated by using the hidden set as the reference indicating what should have been predicted. The evaluation metric, R, proposed in [3] is based on the assumption that each successive item in a list is less likely to be interesting to the user with an exponential decay. This metric uses a parameter, α, which is the position in the ranked list which has a 50-50 chance of being considered by the user. As in [3] we will set α such that the fifth position has a 50-50 chance of being considered.

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Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings by Hosagrahar Visvesvaraya Jagadish⋆ (auth.), David Cheung, Graham J. Williams, Qing Li (eds.)


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