The nature of statistical learning theory
The nature of statistical learning theory
Averaging regularized estimators
Neural Computation
Computation with infinite neural networks
Neural Computation
A sparse representation for function approximation
Neural Computation
An equivalence between sparse approximation and support vector machines
Neural Computation
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
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
The generalized Bayesian committee machine
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling Kernel-Based Systems to Large Data Sets
Data Mining and Knowledge Discovery
Sparse on-line Gaussian processes
Neural Computation
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
The Bayesian Committee Support Vector Machine
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Scaling Large Learning Problems with Hard Parallel Mixtures
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Advanced lectures on machine learning
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Gaussian process mixtures for regression
Statistics and Computing
Healing the relevance vector machine through augmentation
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Bagging for Gaussian process regression
Neurocomputing
Tissue Recognition Approach to Pressure Ulcer Area Estimation with Neural Networks
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Neural Network Optimization for Combinations in Identification Systems
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Kernel carpentry for online regression using randomly varying coefficient model
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multiclass probabilistic kernel discriminant analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
The Journal of Machine Learning Research
Sparse Spectrum Gaussian Process Regression
The Journal of Machine Learning Research
Coupled Gaussian process regression for pose-invariant facial expression recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Tree Decomposition for Large-Scale SVM Problems
The Journal of Machine Learning Research
Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets
The Journal of Machine Learning Research
Adaptive color space switching based approach for face tracking
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Transductive gaussian process regression with automatic model selection
ECML'06 Proceedings of the 17th European conference on Machine Learning
Analysis of some methods for reduced rank gaussian process regression
Switching and Learning in Feedback Systems
Filtered gaussian processes for learning with large data-sets
Switching and Learning in Feedback Systems
Transformations of gaussian process priors
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
GPLP: a local and parallel computation toolbox for Gaussian process regression
The Journal of Machine Learning Research
Detecting RNA sequences using two-stage SVM classifier
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Discriminative fusion of shape and appearance features for human pose estimation
Pattern Recognition
Automatica (Journal of IFAC)
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
Multi-level clustering support vector machine trees for improved protein local structure prediction
International Journal of Data Mining and Bioinformatics
Fast classification for large data sets via random selection clustering and Support Vector Machines
Intelligent Data Analysis
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The Bayesian committee machine (BCM) is a novel approach to combining estimators that were trained on different data sets. Although the BCM can be applied to the combination of any kind of estimators, the main foci are gaussian process regression and related systems such as regularization networks and smoothing splines for which the degrees of freedom increase with the number of training data. Somewhat surprisingly, we find that the performance of the BCM improves if several test points are queried at the same time and is optimal if the number of test points is at least as large as the degrees of freedom of the estimator. The BCM also provides a new solution for on-line learning with potential applications to data mining. We apply the BCM to systems with fixed basis functions and discuss its relationship to gaussian process regression. Finally, we show how the ideas behind the BCM can be applied in a non-Bayesian setting to extend the input-dependent combination of estimators.