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CORR

2011

Springer

2011

Springer

We consider a suboptimal solution path algorithm for the Support Vector Machine. The solution path algorithm is an eﬀective tool for solving a sequence of a parametrized optimization problems in machine learning. The path of the solutions provided by this algorithm are very accurate and they satisfy the optimality conditions more strictly than other SVM optimization algorithms. In many machine learning application, however, this strict optimality is often unnecessary, and it adversely aﬀects the computational eﬃciency. Our algorithm can generate the path of suboptimal solutions within an arbitrary user-speciﬁed tolerance level. It allows us to control the trade-oﬀ between the accuracy of the solution and the computational cost. Moreover, We also show that our suboptimal solutions can be interpreted as the solution of a perturbed optimization problem from the original one. We provide some theoretical analyses of our algorithm based on this novel interpretation. The experiment...

Related Content

Added |
19 Aug 2011 |

Updated |
19 Aug 2011 |

Type |
Journal |

Year |
2011 |

Where |
CORR |

Authors |
Masayuki Karasuyama, Ichiro Takeuchi |

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