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Gaussian process

Known as: GP, Gaussian Processes, Gaussian stochastic process 
In probability theory and statistics, a Gaussian process is a statistical model where observations occur in a continuous domain, e.g. time or space… 
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Related topics

Related topics

34 relations
Additive white Gaussian noiseAutoregressive modelBayesian interpretation of kernel regularizationBrownian motion

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2011
Highly Cited
2011
Variational Gaussian Process Dynamical Systems
High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational… 
Highly Cited
2010
Highly Cited
2010
An Inverse Gaussian Process Model for Degradation Data
This paper studies the maximum likelihood estimation of a class of inverse Gaussian process models for degradation data. Both the… 
Highly Cited
2010
Highly Cited
2010
Gaussian processes with monotonicity information
A method for using monotonicity information in multivariate Gaussian process regression and classification is proposed… 
Review
2009
Review
2009
Gaussian Process modeling of large scale terrain
This paper addresses the problem of large scale terrain modeling for a mobile robot. Building a model of large scale terrain data… 
Highly Cited
2009
Highly Cited
2009
Regression and Classification Using Gaussian Process Priors
Gaussian processes are a natural way of specifying prior distributions over functions of one or more input variables. When such a… 
Highly Cited
2009
Highly Cited
2009
Gaussian process regression with Student-t likelihood
In the Gaussian process regression the observation model is commonly assumed to be Gaussian, which is convenient in computational… 
Highly Cited
2008
Highly Cited
2008
Sparse Convolved Gaussian Processes for Multi-output Regression
We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework… 
Highly Cited
2007
Highly Cited
2007
Active Learning with Gaussian Processes for Object Categorization
Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not… 
Highly Cited
2007
Highly Cited
2007
Discriminative Gaussian process latent variable model for classification
Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may… 
Highly Cited
1995
Highly Cited
1995
Bayesian Learning for Neural Networks
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions… 
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