SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating a class of distance-mapping algorithms for data mining and clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse least squares support vector training in the reduced empirical feature space
Pattern Analysis & Applications
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this paper, we propose a novel learning framework to reform ulate a kernel-based classifier in terms of three modular components: kernel-function determination to incorporate domain knowledge, sparse data representation using FastMap and its variants, and supervised classification performed by using primal form analyzers such as linear SVM. The first important property of this approach is the reusability of the modules: Each module can be easily replaced by its counterparts for a specific learning purpose, e.g., the sparse representation of the data can not only support classification tasks but also be applied in function regression or unsupervised data analysis. Another contribution of the proposed approach is that it enables easy adaption of available primal-form algorithms for nonlinear kernel-based learning. Finally, numerical experiments show that FastMap and SupFM can yield efficient sparse representations with nonlinear kernels. The representation realized better sparsity while maintaining a generalization ability that is comparable to that of the regular SVM classifier.