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BMCBI
2005
251views more  BMCBI 2005»
13 years 8 months ago
Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation
Background: The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very effici...
Tapio Pahikkala, Filip Ginter, Jorma Boberg, Jouni...
ICML
2005
IEEE
14 years 9 months ago
Beyond the point cloud: from transductive to semi-supervised learning
Due to its occurrence in engineering domains and implications for natural learning, the problem of utilizing unlabeled data is attracting increasing attention in machine learning....
Vikas Sindhwani, Partha Niyogi, Mikhail Belkin
ASPLOS
1987
ACM
14 years 10 days ago
Machine-Independent Virtual Memory Management for Paged Uniprocessor and Multiprocessor Architectures
This paper describes the design and implementation of virtual memory management within the CMU Mach Operating System and the experiences gained by the Mach kernel group in porting...
Richard F. Rashid, Avadis Tevanian, Michael Young,...
VLSISP
2011
358views Database» more  VLSISP 2011»
13 years 3 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, ...
SDM
2009
SIAM
161views Data Mining» more  SDM 2009»
14 years 6 months ago
Feature Weighted SVMs Using Receiver Operating Characteristics.
Support Vector Machines (SVMs) are a leading tool in classification and pattern recognition and the kernel function is one of its most important components. This function is used...
Shaoyi Zhang, M. Maruf Hossain, Md. Rafiul Hassan,...