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» Learning of Boolean Functions Using Support Vector Machines
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MINENET
2006
ACM
14 years 3 months ago
SVM learning of IP address structure for latency prediction
We examine the ability to exploit the hierarchical structure of Internet addresses in order to endow network agents with predictive capabilities. Specifically, we consider Suppor...
Robert Beverly, Karen R. Sollins, Arthur Berger
FOCS
2003
IEEE
14 years 2 months ago
Learning DNF from Random Walks
We consider a model of learning Boolean functions from examples generated by a uniform random walk on {0, 1}n . We give a polynomial time algorithm for learning decision trees and...
Nader H. Bshouty, Elchanan Mossel, Ryan O'Donnell,...
IEEEVAST
2010
13 years 3 months ago
ALIDA: Using machine learning for intent discernment in visual analytics interfaces
In this paper, we introduce ALIDA, an Active Learning Intent Discerning Agent for visual analytics interfaces. As users interact with and explore data in a visual analytics enviro...
Tera Marie Green, Ross Maciejewski, Steve DiPaola

Book
640views
15 years 8 months ago
Introduction to Pattern Recognition
"Pattern recognition techniques are concerned with the theory and algorithms of putting abstract objects, e.g., measurements made on physical objects, into categories. Typical...
Sargur Srihari
AUSAI
2008
Springer
13 years 11 months ago
Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling
In recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neocortex and artificial intelligence models of mac...
John Thornton, Jolon Faichney, Michael Blumenstein...