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VLSISP
2011
358views Database» more  VLSISP 2011»
15 years 1 months ago
Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition
Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from superlinear increases in ...
Karthik Nagarajan, Brian Holland, Alan D. George, ...
UAI
2000
15 years 7 months ago
Variational Relevance Vector Machines
The Support Vector Machine (SVM) of Vapnik [9] has become widely established as one of the leading approaches to pattern recognition and machine learning. It expresses predictions...
Christopher M. Bishop, Michael E. Tipping
BMCBI
2006
169views more  BMCBI 2006»
15 years 6 months ago
Machine learning techniques in disease forecasting: a case study on rice blast prediction
Background: Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their ...
Rakesh Kaundal, Amar S. Kapoor, Gajendra P. S. Rag...
MOBISYS
2008
ACM
15 years 8 months ago
MobiVMM: a virtual machine monitor for mobile phones
Mobile phones have evolved into complex systems as they have more and more new applications built-in. As a result, they are less reliable and less secure than before. Virtual Mach...
See-hwan Yoo, Yunxin Liu, Cheol-Ho Hong, Chuck Yoo...
NCA
2008
IEEE
15 years 6 months ago
Polynomial kernel adaptation and extensions to the SVM classifier learning
Three extensions to the Kernel-AdaTron training algorithm for Support Vector Machine classifier learning are presented. These extensions allow the trained classifier to adhere more...
Ramy Saad, Saman K. Halgamuge, Jason Li