Introduction to algorithms
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Example-Based Learning for View-Based Human Face Detection
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
Reducing the run-time complexity in support vector machines
Advances in kernel methods
KDD '99 Proceedings of the fifth 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
Machine Learning
Likelihood-Based Data Squashing: A Modeling Approach to Instance Construction
Data Mining and Knowledge Discovery
Data Squashing by Empirical Likelihood
Data Mining and Knowledge Discovery
Lagrangian support vector machines
The Journal of Machine Learning Research
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Active Learning to Recognize Multiple Types of Plankton
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Bit Reduction Support Vector Machine
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Incremental approximate matrix factorization for speeding up support vector machines
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable non-linear Support Vector Machine using hierarchical clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A novel and quick SVM-based multi-class classifier
Pattern Recognition
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass core vector machine
Proceedings of the 24th international conference on Machine learning
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Recognizing plankton images from the shadow image particle profiling evaluation recorder
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast accurate fuzzy clustering through data reduction
IEEE Transactions on Fuzzy Systems
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Parallel sequential minimal optimization for the training of support vector machines
IEEE Transactions on Neural Networks
Comparing machine learning approaches for context-aware composition
SC'11 Proceedings of the 10th international conference on Software composition
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Support vector machines (SVMs) can be trained to be very accurate classifiers and have been used in many applications. However, the training time and, to a lesser extent, prediction time of SVMs on very large data sets can be very long. This paper presents a fast compression method to scale up SVMs to large data sets. A simple bit-reduction method is applied to reduce the cardinality of the data by weighting representative examples. We then develop SVMs trained on the weighted data. Experiments indicate that bit-reduction SVM produces a significant reduction in the time required for both training and prediction with minimum loss in accuracy. It is also shown to typically be more accurate than random sampling when the data are not overcompressed.