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» Learning Rules and Their Exceptions
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TNN
2010
155views Management» more  TNN 2010»
14 years 8 months ago
Incorporating the loss function into discriminative clustering of structured outputs
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations ac...
Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. ...
ICPR
2006
IEEE
16 years 2 months ago
Dissimilarity-based classification for vectorial representations
General dissimilarity-based learning approaches have been proposed for dissimilarity data sets [11, 10]. They arise in problems in which direct comparisons of objects are made, e....
Elzbieta Pekalska, Robert P. W. Duin
IAT
2007
IEEE
15 years 8 months ago
Investigating User Browsing Behavior
This paper describes our efforts to investigate factors in user browsing behavior to automatically evaluate Web pages that the user shows interest in. We developed a client site l...
Ganesan Velayathan, Seiji Yamada
FSKD
2007
Springer
277views Fuzzy Logic» more  FSKD 2007»
15 years 7 months ago
Autonomous Robot Control Using Evidential Reasoning
Evidence theory has been widely applied to uncertainty reasoning. In this paper a finite state machine with evidential reasoning is proposed to control autonomous robots. The Khep...
Qingxiang Wu, David A. Bell, Rashid Hafeez Khokhar...
58
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ASPLOS
2006
ACM
15 years 7 months ago
Automatic generation of peephole superoptimizers
Peephole optimizers are typically constructed using human-written pattern matching rules, an approach that requires expertise and time, as well as being less than systematic at ex...
Sorav Bansal, Alex Aiken