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NIPS
2008
13 years 10 months ago
Partially Observed Maximum Entropy Discrimination Markov Networks
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
Jun Zhu, Eric P. Xing, Bo Zhang
GIS
2008
ACM
14 years 9 months ago
Pedestrian flow prediction in extensive road networks using biased observational data
In this paper, we discuss an application of spatial data mining to predict pedestrian flow in extensive road networks using a large biased sample. Existing out-of-the-box techniqu...
Michael May, Simon Scheider, Roberto Rösler, ...
ACL
2006
13 years 10 months ago
Modeling Human Sentence Processing Data with a Statistical Parts-of-Speech Tagger
It has previously been assumed in the psycholinguistic literature that finite-state models of language are crucially limited in their explanatory power by the locality of the prob...
Jihyun Park
IROS
2008
IEEE
211views Robotics» more  IROS 2008»
14 years 2 months ago
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
Jonathan Ko, Dieter Fox
ICML
2004
IEEE
14 years 9 months ago
Learning low dimensional predictive representations
Predictive state representations (PSRs) have recently been proposed as an alternative to partially observable Markov decision processes (POMDPs) for representing the state of a dy...
Matthew Rosencrantz, Geoffrey J. Gordon, Sebastian...