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» Learning of Boolean Functions Using Support Vector Machines
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DAC
2008
ACM
14 years 8 months ago
Functional test selection based on unsupervised support vector analysis
Extensive software-based simulation continues to be the mainstream methodology for functional verification of designs. To optimize the use of limited simulation resources, coverag...
Onur Guzey, Li-C. Wang, Jeremy R. Levitt, Harry Fo...
PKDD
2009
Springer
138views Data Mining» more  PKDD 2009»
14 years 2 months ago
Margin and Radius Based Multiple Kernel Learning
A serious drawback of kernel methods, and Support Vector Machines (SVM) in particular, is the difficulty in choosing a suitable kernel function for a given dataset. One of the appr...
Huyen Do, Alexandros Kalousis, Adam Woznica, Melan...
SEKE
2004
Springer
14 years 1 months ago
Supporting the Requirements Prioritization Process. A Machine Learning approach
Requirements prioritization plays a key role in the requirements engineering process, in particular with respect to critical tasks such as requirements negotiation and software re...
Paolo Avesani, Cinzia Bazzanella, Anna Perini, Ang...
SIGIR
2000
ACM
14 years 3 days ago
Hierarchical classification of Web content
This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train diffe...
Susan T. Dumais, Hao Chen
ICML
2007
IEEE
14 years 8 months ago
Beamforming using the relevance vector machine
Beamformers are spatial filters that pass source signals in particular focused locations while suppressing interference from elsewhere. The widely-used minimum variance adaptive b...
David P. Wipf, Srikantan S. Nagarajan