Instance-Based Learning Algorithms
Machine Learning
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Two algorithms for nearest-neighbor search in high dimensions
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Properties of support vector machines
Neural Computation
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Lower bounds for high dimensional nearest neighbor search and related problems
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Multidimensional binary search trees used for associative searching
Communications of the ACM
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning Changing Concepts by Exploiting the Structure of Change
Machine Learning
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
IEEE Intelligent Systems
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Applications of Support Vector Machines for Pattern Recognition: A Survey
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Gender Classification with Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
On Approximate Nearest Neighbors in Non-Euclidean Spaces
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
SVM-KM: Speeding SVMs Learning with a priori Cluster Selection and k-Means
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
A Training Method with Small Computation for Classification
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Considerations about sample-size sensitivity of a family of editednearest-neighbor rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The training of neural classifiers with condensed datasets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast nearest neighbor search of entropy-constrained vector quantization
IEEE Transactions on Image Processing
Sample selection via clustering to construct support vector-like classifiers
IEEE Transactions on Neural Networks
Hit Miss Networks with Applications to Instance Selection
The Journal of Machine Learning Research
Graph-Based Discrete Differential Geometry for Critical Instance Filtering
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Linear boundary discriminant analysis
Pattern Recognition
A new discriminant analysis based on boundary/non-boundary pattern separation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Bootstrap based pattern selection for support vector regression
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Multi-weight vector projection support vector machines
Pattern Recognition Letters
Selecting training points for one-class support vector machines
Pattern Recognition Letters
Information Sciences: an International Journal
Pattern selection for support vector regression based response modeling
Expert Systems with Applications: An International Journal
Support vector machines training data selection using a genetic algorithm
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Fast instance selection for speeding up support vector machines
Knowledge-Based Systems
Robust predictive model for evaluating breast cancer survivability
Engineering Applications of Artificial Intelligence
Neighbors' distribution property and sample reduction for support vector machines
Applied Soft Computing
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The support vector machine (SVM) has been spotlighted in the machine learning community because of its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and a long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training.