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UAI
1998
15 years 7 months ago
Learning the Structure of Dynamic Probabilistic Networks
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
Nir Friedman, Kevin P. Murphy, Stuart J. Russell
EACL
1993
ACL Anthology
15 years 7 months ago
Parsing the Wall Street Journal with the Inside-Outside Algorithm
We report grammar inference experiments on partially parsed sentences taken from the Wall Street Journal corpus using the inside-outside algorithm for stochastic context-free gram...
Yves Schabes, Michal Roth, Randy Osborne
CORR
2011
Springer
168views Education» more  CORR 2011»
15 years 1 months ago
Limit Theorems for the Sample Entropy of Hidden Markov Chains
The Shannon-McMillan-Breiman theorem asserts that the sample entropy of a stationary and ergodic stochastic process converges to the entropy rate of the same process almost surely...
Guangyue Han
ICPR
2006
IEEE
16 years 7 months ago
Onset Detection through Maximal Redundancy Detection
We propose a criterion, called `maximal redundancy', for onset detection in time series. The concept redundancy is adopted from information theory and indicates how well a si...
Gert Van Dijck, Marc M. Van Hulle
ICDE
2007
IEEE
187views Database» more  ICDE 2007»
16 years 7 months ago
RFID Data Processing with a Data Stream Query Language
RFID technology provides significant advantages over traditional object-tracking technology and is increasingly adopted and deployed in real applications. RFID applications genera...
Yijian Bai, Fusheng Wang, Peiya Liu, Carlo Zaniolo...