A Modified SMO Algorithm for SVM Regression and Its Application in Quality Prediction of HP-LDPE

@inproceedings{Zhao2005AMS,
  title={A Modified SMO Algorithm for SVM Regression and Its Application in Quality Prediction of HP-LDPE},
  author={Hengping Zhao and Jinshou Yu},
  booktitle={ICNC},
  year={2005}
}
A modified sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression is proposed based on Shevade's SMO-1 algorithm. The main improvement is that a modified heuristics method is used in this modified SMO algorithm to choose the first Lagrange multiplier when optimizing the Lagrange multipliers corresponding to the non-boundary examples. To illustrate the validity of the proposed modified SMO algorithm, a benchmark dataset and a practical application in… 
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References

SHOWING 1-10 OF 14 REFERENCES
Improvements to the SMO algorithm for SVM regression
TLDR
Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression that perform significantly faster than the original SMO on the datasets tried.
Efficient SVM Regression Training with SMO
TLDR
This work generalizes SMO so that it can handle regression problems, and addresses problems with several modifications that enable caching to be effectively used with SMO.
An Improvement Algorithm to Sequential Minimal Optimization
TLDR
A novel strategy for selecting working sets applied in SMO takes both reduction of the object function and computational cost related to the selected working set into consideration in order to improve the efficiency of the kernel cache.
Support Vector Machines for Regression
TLDR
An overview of the basic ideas underlying SVM for regression, an important and promising new direction in the area of nonlinear parameter identification, forecast, modeling and control, are given in this paper.
A fast iterative nearest point algorithm for support vector machine classifier design
TLDR
Comparative computational evaluation of the new fast iterative algorithm against powerful SVM methods such as Platt's sequential minimal optimization shows that the algorithm is very competitive.
Fast training of support vector machines using sequential minimal optimization, advances in kernel methods
TLDR
SMO breaks this large quadratic programming problem into a series of smallest possible QP problems, which avoids using a time-consuming numerical QP optimization as an inner loop and hence SMO is fastest for linear SVMs and sparse data sets.
An improved training algorithm for support vector machines
  • E. Osuna, R. Freund, F. Girosi
  • Computer Science
    Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop
  • 1997
TLDR
This paper presents a decomposition algorithm that is guaranteed to solve the QP problem and that does not make assumptions on the expected number of support vectors.
Advances in kernel methods: support vector learning
TLDR
Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
A tutorial on support vector regression
TLDR
This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
UCI Repository of machine learning databases
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