A resource-allocating network for function interpolation
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line learning and stochastic approximations
On-line learning in neural networks
A Bayesian approach to on-line learning
On-line learning in neural networks
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Finite-dimensional approximation of Gaussian processes
Proceedings of the 1998 conference on Advances in neural information processing systems II
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Neural Computation
Expectation propagation for approximate Bayesian inference
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
On different facets of regularization theory
Neural Computation
Approximation bounds for some sparse kernel regression algorithms
Neural Computation
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
The em algorithm for kernel matrix completion with auxiliary data
The Journal of Machine Learning Research
An approximate analytical approach to resampling averages
The Journal of Machine Learning Research
Gaussian process classification for segmenting and annotating sequences
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Healing the relevance vector machine through augmentation
ICML '05 Proceedings of the 22nd international conference on Machine learning
2005 Special Issue: Constructing Bayesian formulations of sparse kernel learning methods
Neural Networks - 2005 Special issue: IJCNN 2005
Assessing Approximate Inference for Binary Gaussian Process Classification
The Journal of Machine Learning Research
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Fast generalized cross-validation algorithm for sparse model learning
Neural Computation
Bayes Machines for binary classification
Pattern Recognition Letters
Sparse multiscale gaussian process regression
Proceedings of the 25th international conference on Machine learning
Boosting RVM Classifiers for Large Data Sets
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Source Separation with Gaussian Process Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
Adaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression
Expert Systems with Applications: An International Journal
Online Sparse Matrix Gaussian Process Regression and Vision Applications
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Visual Tracking Using Particle Filters with Gaussian Process Regression
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Bagging for Gaussian process regression
Neurocomputing
Validation-based sparse gaussian process classifier design
Neural Computation
Feature Selection for Value Function Approximation Using Bayesian Model Selection
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Gaussian process regressors for multiuser detection in DS-CDMA systems
IEEE Transactions on Communications
Extended kernel recursive least squares algorithm
IEEE Transactions on Signal Processing
Sparse online model learning for robot control with support vector regression
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Bounded Kernel-Based Online Learning
The Journal of Machine Learning Research
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
The Journal of Machine Learning Research
Joint nonlinear channel equalization and soft LDPC decoding with Gaussian processes
IEEE Transactions on Signal Processing
Virtual vector machine for Bayesian online classification
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Sparse approximation through boosting for learning large scale kernel machines
IEEE Transactions on Neural Networks
Sparse Spectrum Gaussian Process Regression
The Journal of Machine Learning Research
INTAMAP: The design and implementation of an interoperable automated interpolation web service
Computers & Geosciences
Adaptive compression for 3D laser data
International Journal of Robotics Research
ACM Transactions on Interactive Intelligent Systems (TiiS)
Regularized sparse Kernel slow feature analysis
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Online SVR Training by Solving the Primal Optimization Problem
Journal of Signal Processing Systems
A kernel-based Perceptron with dynamic memory
Neural Networks
Fast sparse multinomial regression applied to hyperspectral data
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Sparse gaussian processes using backward elimination
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Analysis of some methods for reduced rank gaussian process regression
Switching and Learning in Feedback Systems
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Temporal-Spatial local gaussian process experts for human pose estimation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
PLISS: labeling places using online changepoint detection
Autonomous Robots
Dynamic GP models: an overview and recent developments
ASM'12 Proceedings of the 6th international conference on Applied Mathematics, Simulation, Modelling
Adaptive data compression for robot perception
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Estimating the non-linear dynamics of free-flying objects
Robotics and Autonomous Systems
Variational multinomial logit gaussian process
The Journal of Machine Learning Research
Online learning with multiple kernels: A review
Neural Computation
Fixed budget quantized kernel least-mean-square algorithm
Signal Processing
Teaching and leading an ad hoc teammate: Collaboration without pre-coordination
Artificial Intelligence
Construction of approximation spaces for reinforcement learning
The Journal of Machine Learning Research
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We develop an approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method is based on a Combination of a Bayesian on-line algorithm, together with a sequential construction of a relevant subsample of the data that fully specifies the prediction of the GP model. By using an appealing parameterization and projection techniques in a reproducing kernel Hilbert space, recursions for the effective parameters and a sparse gaussian approximation of the posterior process are obtained. This allows for both a propagation of predictions and Bayesian error measures. The significance and robustness of our approach are demonstrated on a variety of experiments.