Matrix analysis
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Elements of information theory
Elements of information theory
Bayesian methods for adaptive models
Bayesian methods for adaptive models
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
The nature of statistical learning theory
The nature of statistical learning theory
Playing billiards in version space
Neural Computation
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variational methods for graphical models
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Kernel principal component analysis
Advances in kernel methods
Learning nonlinear overcomplete representations for efficient coding
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Mean field methods for classification with Gaussian processes
Proceedings of the 1998 conference on Advances in neural information processing systems II
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Data selection for support vector machine classifiers
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
AI Game Programming Wisdom
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Some Sparse Approximation Bounds for Regression Problems
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection and Dualities in Maximum Entropy Discrimination
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Variational Relevance Vector Machines
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A Column Generation Algorithm For Boosting
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Models of Noise and Robust Estimation
Models of Noise and Robust Estimation
Maximum entropy discrimination
Maximum entropy discrimination
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Neural Computation
Computing upper and lower bounds on likelihoods in intractable networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Model-based transductive learning of the kernel matrix
Machine Learning
Nonparametric identification of population models via Gaussian processes
Automatica (Journal of IFAC)
Wavelet estimation by Bayesian thresholding and model selection
Automatica (Journal of IFAC)
A new kernel-based approach for linear system identification
Automatica (Journal of IFAC)
Facial expression recognition in JAFFE dataset based on Gaussian process classification
IEEE Transactions on Neural Networks
Prediction error identification of linear systems: A nonparametric Gaussian regression approach
Automatica (Journal of IFAC)
Consistent identification of Wiener systems: A machine learning viewpoint
Automatica (Journal of IFAC)
The Journal of Machine Learning Research
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Bayesian methods allow for a simple and intuitive representation of the function spaces used by kernel methods. This chapter describes the basic principles of Gaussian Processes, their implementation and their connection to other kernel-based Bayesian estimation methods, such as the Relevance Vector Machine.