Corpus ID: 1099857

Fast training of support vector machines using sequential minimal optimization, advances in kernel methods

@inproceedings{Platt1999FastTO,
  title={Fast training of support vector machines using sequential minimal optimization, advances in kernel methods},
  author={John C. Platt},
  year={1999}
}
This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO. [...] Key Result For the MNIST database, SMO is as fast as PCG chunking; while for the UCI Adult database and linear SVMs, SMO can be more than 1000 times faster than the PCG chunking algorithm.Expand
An SMO Algorithm for the Potential Support Vector Machine
Support Vector Machines for Regression Problems with Sequential Minimal Optimization
Condensed Vector Machines: Learning Fast Machine for Large Data
Training Support Vector Machines using Frank-Wolfe Optimization Methods
Scaling up support vector machines
Fast training Support Vector Machines using parallel sequential minimal optimization
Selecting Data for Fast Support Vector Machines Training
Generalized Core Vector Machines
Large-Scale Support Vector Machines: Algorithms and Theory
Efficient SVM Regression Training with SMO
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