Neural networks and the bias/variance dilemma
Neural Computation
Neural Computation
A practical Bayesian framework for backpropagation networks
Neural Computation
Bayesian regularization and pruning using a Laplace prior
Neural Computation
Machine Learning
Matrix computations (3rd ed.)
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Predictive automatic relevance determination by expectation propagation
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Optimally Regularised Kernel Fisher Discriminant Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Fast Dual Algorithm for Kernel Logistic Regression
Machine Learning
Predictive Approaches for Choosing Hyperparameters in Gaussian Processes
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Optimally regularised kernel Fisher discriminant classification
Neural Networks
Adaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression
Expert Systems with Applications: An International Journal
Model selection for the LS-SVM. Application to handwriting recognition
Pattern Recognition
Model Selection: Beyond the Bayesian/Frequentist Divide
The Journal of Machine Learning Research
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
The Journal of Machine Learning Research
Model selection for least squares support vector regressions based on small-world strategy
Expert Systems with Applications: An International Journal
Quadratically constrained maximum a posteriori estimation for binary classifier
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Nyström approximate model selection for LSSVM
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Label-Noise robust logistic regression and its applications
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Eigenvalues perturbation of integral operator for kernel selection
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Advances in Artificial Neural Systems
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While the model parameters of a kernel machine are typically given by the solution of a convex optimisation problem, with a single global optimum, the selection of good values for the regularisation and kernel parameters is much less straightforward. Fortunately the leave-one-out cross-validation procedure can be performed or a least approximated very efficiently in closed form for a wide variety of kernel learning methods, providing a convenient means for model selection. Leave-one-out cross-validation based estimates of performance, however, generally exhibit a relatively high variance and are therefore prone to over-fitting. In this paper, we investigate the novel use of Bayesian regularisation at the second level of inference, adding a regularisation term to the model selection criterion corresponding to a prior over the hyper-parameter values, where the additional regularisation parameters are integrated out analytically. Results obtained on a suite of thirteen real-world and synthetic benchmark data sets clearly demonstrate the benefit of this approach.