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» Nonlinear Predictive Control with a Gaussian Process Model
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BMCBI
2007
194views more  BMCBI 2007»
13 years 5 months ago
Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
Xin Zhao, Leo Wang-Kit Cheung
NIPS
2008
13 years 6 months ago
Local Gaussian Process Regression for Real Time Online Model Learning
Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by...
Duy Nguyen-Tuong, Matthias Seeger, Jan Peters
ICDM
2010
IEEE
264views Data Mining» more  ICDM 2010»
13 years 3 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
CORR
2010
Springer
168views Education» more  CORR 2010»
13 years 3 months ago
Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by...
Ricardo Silva
AAAI
2006
13 years 6 months ago
Mixtures of Predictive Linear Gaussian Models for Nonlinear, Stochastic Dynamical Systems
The Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent parame...
David Wingate, Satinder P. Singh