The Strength of Weak Learnability
Machine Learning
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
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Finite-dimensional approximation of Gaussian processes
Proceedings of the 1998 conference on Advances in neural information processing systems II
The generalized Bayesian committee machine
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards scalable support vector machines using squashing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Neural Computation
Scaling mining algorithms to large databases
Communications of the ACM - Evolving data mining into solutions for insights
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
GE-CKO: A Method to Optimize Composite Kernels for Web Page Classification
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Concept boundary detection for speeding up SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Kernel rewards regression: an information efficient batch policy iteration approach
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Addressing the problems of data-centric physiology-affect relations modeling
Proceedings of the 15th international conference on Intelligent user interfaces
Tree Decomposition for Large-Scale SVM Problems
The Journal of Machine Learning Research
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In the form of the support vector machine and Gaussian processes, kernel-based systems are currently very popular approaches to supervised learning. Unfortunately, the computational load for training kernel-based systems increases drastically with the size of the training data set, such that these systems are not ideal candidates for applications with large data sets. Nevertheless, research in this direction is very active. In this paper, I review some of the current approaches toward scaling kernel-based systems to large data sets.