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Gaussian Processes for Machine Learning
TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
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The Pascal Visual Object Classes (VOC) Challenge
TLDR
The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and includes detailed algorithms for supervised-learning problem for both regression and classification.
The Pascal Visual Object Classes Challenge: A Retrospective
TLDR
A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
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Using the Nyström Method to Speed Up Kernel Machines
TLDR
It is shown that an approximation to the eigendecomposition of the Gram matrix can be computed by the Nystrom method (which is used for the numerical solution of eigenproblems) and the computational complexity of a predictor using this approximation is O(m2n).
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The PASCAL visual object classes challenge 2006 (VOC2006) results
This report presents the results of the 2006 PASCAL Visual Object Classes Challenge (VOC2006). Details of the challenge, data, and evaluation are presented. Participants in the challenge submitted…
GTM: The Generative Topographic Mapping
TLDR
A form of nonlinear latent variable model called the generative topographic mapping, for which the parameters of the model can be determined using the expectation-maximization algorithm, is introduced.
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Multi-task Gaussian Process Prediction
TLDR
A model that learns a shared covariance function on input-dependent features and a "free-form" covariance matrix over tasks allows for good flexibility when modelling inter-task dependencies while avoiding the need for large amounts of data for training.
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Gaussian Processes for Regression
TLDR
This paper investigates the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations.
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Bayesian Classification With Gaussian Processes
TLDR
A Bayesian treatment is provided, integrating over uncertainty in y and in the parameters that control the Gaussian process prior the necessary integration over y is carried out using Laplace's approximation, and the method is generalized to multiclass problems (m>2) using the softmax function.
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