A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Interior-Point Methods for Massive Support Vector Machines
SIAM Journal on Optimization
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Fast methods for kernel-based text analysis
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Japanese dependency structure analysis based on support vector machines
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Infinite-σ Limits For Tikhonov Regularization
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Fast logistic regression for text categorization with variable-length n-grams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Feature hashing for large scale multitask learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
splitSVM: fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Sparse Online Learning via Truncated Gradient
The Journal of Machine Learning Research
Labeled pseudo-projective dependency parsing with support vector machines
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Hash Kernels for Structured Data
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Kernel slicing: scalable online training with conjunctive features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Deterministic statistical mapping of sentences to underspecified semantics
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Reinforcement learning with a bilinear q function
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Inhibition in multiclass classification
Neural Computation
The bitvector machine: a fast and robust machine learning algorithm for non-linear problems
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
The Journal of Machine Learning Research
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Kernel techniques have long been used in SVM to handle linearly inseparable problems by transforming data to a high dimensional space, but training and testing large data sets is often time consuming. In contrast, we can efficiently train and test much larger data sets using linear SVM without kernels. In this work, we apply fast linear-SVM methods to the explicit form of polynomially mapped data and investigate implementation issues. The approach enjoys fast training and testing, but may sometimes achieve accuracy close to that of using highly nonlinear kernels. Empirical experiments show that the proposed method is useful for certain large-scale data sets. We successfully apply the proposed method to a natural language processing (NLP) application by improving the testing accuracy under some training/testing speed requirements.