From regularization operators to support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Dynamically adapting kernels in support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Introduction to the Special Issue on Meta-Learning
Machine Learning
Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features
Proceedings of the 2006 ACM symposium on Applied computing
Mindful: A framework for Meta-INDuctive neuro-FUzzy Learning
Information Sciences: an International Journal
Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Case-Based Reasoning and the Statistical Challenges
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Meta-learning optimal parameter values in non-stationary environments
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Active Generation of Training Examples in Meta-Regression
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Simultaneous tuning of hyperparameter and parameter for support vector machines
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Incorporating prior domain knowledge into a kernel based feature selection algorithm
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
DS'10 Proceedings of the 13th international conference on Discovery science
Grey relational grade in local support vector regression for financial time series prediction
Expert Systems with Applications: An International Journal
Meta-learning experiences with the mindful system
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Meta-data: characterization of input features for meta-learning
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Relaxation of hard classification targets for LSE minimization
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
Pairwise meta-rules for better meta-learning-based algorithm ranking
Machine Learning
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The Support Vector Machine algorithm is sensitive to the choice of parameter settings. If these are not set correctly, the algorithm may have a substandard performance. Suggesting a good setting is thus an important problem. We propose a meta-learning methodology for this purpose and exploit information about the past performance of different settings. The methodology is applied to set the width of the Gaussian kernel. We carry out an extensive empirical evaluation, including comparisons with other methods (fixed default ranking; selection based on cross-validation and a heuristic method commonly used to set the width of the SVM kernel). We show that our methodology can select settings with low error while providing significant savings in time. Further work should be carried out to see how the methodology could be adapted to different parameter setting tasks.