An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multidimensional binary search trees used for associative searching
Communications of the ACM
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Journal of Computer and System Sciences
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
The Journal of Machine Learning Research
Statistical Learning and Data Science
Statistical Learning and Data Science
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In this paper we present and evaluate a simple but effective machine learning algorithm that we call Bitvector Machine: Feature vectors are partitioned along component-wise quantiles and converted into bitvectors that are learned. It is shown that the method is efficient in both training and classification. The effectiveness of the method is analysed theoretically for best and worst-case scenarios. Experiments on high-dimensional synthetic and real world data show a huge speed boost compared to Support Vector Machines with RBF kernel. By tabulating kernel functions, computing medians in linear-time, and exploiting modern processor technology for advanced bitvector operations, we achieve a speed-up of 32 for classification and 48 for kernel evaluation compared to the popular LIBSVM. Although the method does not generally outperform a SVM with RBF kernel it achieves a high classification accuracy and has qualitative advantages over the linear classifier.