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IAT
2008
IEEE
16 years 1 months ago
Finding Minimum Data Requirements Using Pseudo-independence
In situations where Bayesian networks (BN) inferencing approximation is allowable, we show how to reduce the amount of sensory observations necessary and in a multi-agent context ...
Yoonheui Kim, Victor R. Lesser
NIPS
2008
15 years 8 months ago
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov ...
Tran The Truyen, Dinh Q. Phung, Hung Hai Bui, Svet...
JCNS
2010
104views more  JCNS 2010»
15 years 5 months ago
A new look at state-space models for neural data
State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely o...
Liam Paninski, Yashar Ahmadian, Daniel Gil Ferreir...
IGARSS
2010
15 years 4 months ago
Monitoring air and Land Surface Temperatures from remotely sensed data for climate-human health applications
This study proposes a methodology to infer maximum air temperature from space using observations from polar orbiting satellite MODIS. A previous study showed that minimum Land Sur...
Pietro Ceccato, Christelle Vancutsem, Marouane Tem...
ICSOC
2009
Springer
15 years 4 months ago
An Initial Proposal for Data-Aware Resource Analysis of Orchestrations with Applications to Predictive Monitoring
Several activities in service oriented computing can benefit from knowing ahead of time future properties of a given service composition. In this paper we focus on how statically i...
Dragan Ivanovic, Manuel Carro, Manuel V. Hermenegi...