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}
}
  • John C. Platt
  • Published 1999
  • Computer Science, Mathematics
  • 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 Abstract
    Support Vector Machines for Regression Problems with Sequential Minimal Optimization
    • 3
    An SMO Algorithm for the Potential Support Vector Machine
    • 25
    • Highly Influenced
    • PDF
    Condensed Vector Machines: Learning Fast Machine for Large Data
    • 14
    Training Support Vector Machines using Frank-Wolfe Optimization Methods
    • 7
    • Highly Influenced
    • PDF
    Scaling up support vector machines
    Generalized Core Vector Machines
    • 151
    • PDF
    Efficient SVM Regression Training with SMO
    • 267
    • PDF
    Large-Scale Support Vector Machines: Algorithms and Theory
    • 48
    • Highly Influenced
    • PDF