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» Modelling Smooth Paths Using Gaussian Processes
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ICASSP
2009
IEEE
14 years 5 months ago
Acoustic fall detection using Gaussian mixture models and GMM supervectors
We present a system that detects human falls in the home environment, distinguishing them from competing noise, by using only the audio signal from a single far-field microphone....
Xiaodan Zhuang, Jing Huang, Gerasimos Potamianos, ...
ICIP
2007
IEEE
15 years 19 days ago
Faithful Shape Representation for 2D Gaussian Mixtures
It has been recently discovered that a faithful representation for the shape of some simple distributions can be constructed using invariant statistics [1, 2]. In this paper, we c...
Mireille Boutin, Mary I. Comer
CSDA
2011
13 years 6 months ago
Approximate forward-backward algorithm for a switching linear Gaussian model
Motivated by the application of seismic inversion in the petroleum industry we consider a hidden Markov model with two hidden layers. The bottom layer is a Markov chain and given ...
Hugo Hammer, Håkon Tjelmeland
ICDM
2010
IEEE
264views Data Mining» more  ICDM 2010»
13 years 9 months ago
Block-GP: Scalable Gaussian Process Regression for Multimodal Data
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
Kamalika Das, Ashok N. Srivastava
JMLR
2008
159views more  JMLR 2008»
13 years 11 months ago
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to ...
Andreas Krause, Ajit Paul Singh, Carlos Guestrin