Communications for Statistical Applications and Methods

  • 제6권3호
  • /
  • Pages.843-848
  • /
  • 1999
  • /
  • 2287-7843(pISSN)
  • /
  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지

Kernel Adatron Algorithm for Supprot Vector Regression

  • Kyungha Seok (Dept. of Data Science, Inje University) ;
  • Changha Hwang (Dept. of Statistical Information, Catholic University)
  • 발행 : 1999.12.01
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초록

Support vector machine(SVM) is a new and very promising classification and regression technique developed by Bapnik and his group at AT&T Bell laboratories. However it has failed to establish itself as common machine learning tool. This is partly due to the fact that SVM is not easy to implement and its standard implementation requires the optimization package for quadratic programming. In this paper we present simple iterative Kernl Adatron algorithm for nonparametric regression which is easy to implement and guaranteed to converge to the optimal solution and compare it with neural networks and projection pursuit regression.

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참고문헌

  1. Dept. of Engineering Mathematics Technical Report Simple Learning Algorithms for Training Support Vector Machines Campbell, C.;Cristianini, N.
  2. IEEE Transactions on Neural Networks v.5 A robust Back Propagation Learning Algorithm for Function Approximation Chen, D.S.;Jain, R.C.
  3. NeuroCOLT2 Technical Report Dynamically Adapting Kernels in Support Vector Machines Cristianini, N.;Campbell, C.;Shawe-Talor, J.
  4. Support Vector Machines for Classification and Regression Gunn, S.
  5. IEEE Transactions on Neural Networks v.5 Regression Modeling in Back-Propagation and Projection Pursuit Learning Hwang, J.N.;Lay, S.R.;Maechler, M.;Martin, D.;Schimert, J.
  6. Model Selection Using Statistical Learning Theory Shao, X.
  7. NeuroCOLT2 Technical Report A Tutorial in Support Vector Regression Smola, A.J.;Scholkopf, B.
  8. The Nature of Statistical Learning Theory Vapnik, V.
  9. Statistical Learning Theory Vapnik, V.