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KDD
2006
ACM
165views Data Mining» more  KDD 2006»
16 years 5 months ago
Training linear SVMs in linear time
Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like t...
Thorsten Joachims
ICFP
2002
ACM
16 years 4 months ago
Packrat parsing: : simple, powerful, lazy, linear time, functional pearl
Packrat parsing is a novel technique for implementing parsers in a lazy functional programming language. A packrat parser provides the power and flexibility of top-down parsing wi...
Bryan Ford
PKDD
2009
Springer
155views Data Mining» more  PKDD 2009»
15 years 11 months ago
Dynamic Factor Graphs for Time Series Modeling
Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
Piotr W. Mirowski, Yann LeCun
MICRO
2008
IEEE
106views Hardware» more  MICRO 2008»
15 years 11 months ago
EVAL: Utilizing processors with variation-induced timing errors
Parameter variation in integrated circuits causes sections of a chip to be slower than others. If, to prevent any resulting timing errors, we design processors for worst-case para...
Smruti R. Sarangi, Brian Greskamp, Abhishek Tiwari...
APN
2005
Springer
15 years 10 months ago
Continuization of Timed Petri Nets: From Performance Evaluation to Observation and Control
State explosion is a fundamental problem in the analysis and synthesis of discrete event systems. Continuous Petri nets can be seen as a relaxation of discrete models allowing more...
Manuel Silva, Laura Recalde