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UAI
1998
15 years 5 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 5 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»
14 years 10 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 4 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 5 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...