Neural Computation
Local algorithms for pattern recognition and dependencies estimation
Neural Computation
Large Margin Trees for Induction and Transduction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Support Vector Machines
Consistency and Localizability
The Journal of Machine Learning Research
Efficient Algorithm for Localized Support Vector Machine
IEEE Transactions on Knowledge and Data Engineering
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
Tree Decomposition for Large-Scale SVM Problems
The Journal of Machine Learning Research
On qualitative robustness of support vector machines
Journal of Multivariate Analysis
Consistency of support vector machines using additive kernels for additive models
Computational Statistics & Data Analysis
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In supervised learning problems, global and local learning algorithms are used. In contrast to global learning algorithms, the prediction of a local learning algorithm in a testing point is only based on training data which are close to the testing point. Every global algorithm such as support vector machines (SVM) can be localized in the following way: in every testing point, the (global) learning algorithm is not applied to the whole training data but only to the k nearest neighbors (kNN) of the testing point. In case of support vector machines, the success of such mixtures of SVM and kNN (called SVM-KNN) has been shown in extensive simulation studies and also for real data sets but only little has been known on theoretical properties so far. In the present article, it is shown how a large class of regularized kernel methods (including SVM) can be localized in order to get a universally consistent learning algorithm.