Article
Full entry |
PDF
(0.9 MB)
Feedback
PDF
(0.9 MB)
Feedback
Keywords:
piecewise linear classifiers; clustering; recognition rates
piecewise linear classifiers; clustering; recognition rates
Summary:
We propose a new method to construct piecewise linear classifiers. This method constructs hyperplanes of a piecewise linear classifier so as to keep the correct recognition rate over a threshold for a training set. The threshold is determined automatically by the MDL (Minimum Description Length) criterion so as to avoid overfitting of the classifier to the training set. The proposed method showed better results in some experiments than a previous method.
References:
[2] Forgy E. W.: Cluster analysis of multivariate data: Efficiency versus interpretability of classifications. Abstracts Biometrics 21 (1965), 3, 768
[3] al S. Hayamizu et: Generation of VCV/CVC balanced word sets for speech database. Bull. Electrotechnical Laboratory 49 (1985), 10, 803–834
[4] Mangasarian O. L.: Multisurface method of pattern separation. IEEE Trans. Inform. Theory 14 (1968), 6, 801–807 DOI 10.1109/TIT.1968.1054229 | Zbl 0169.51108
[5] Meisel W. S.: Computer–Oriented Approaches to Pattern Recognition. Academic Press, New York 1972 Zbl 0252.68063
[6] Nilsson N. J.: Learning Machines. McGraw–Hill, New York 1965 Zbl 0760.68060
[7] Park Y., Sklansky J.: Automated design of multiple–class piecewise linear classifiers. J. Classification 6 (1989), 195–222 DOI 10.1007/BF01908599 | Zbl 0698.62061
[8] Rissanen J.: A universal prior for integers and estimation by minimum description length. Ann. Statist. 11 (1983), 416–431 DOI 10.1214/aos/1176346150 | Zbl 0513.62005
[9] Sklansky J., Michelotti L.: Locally trained piecewise linear classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence 2 (1980), 101–111 DOI 10.1109/TPAMI.1980.4766988 | Zbl 0443.62046
[10] Sklansky J., Wassel G. N.: Pattern Classification and Trainable Machines. Springer–Verlag, New York 1981
[11] Takiyama R.: A learning procedure for multisurface method of pattern separation. Pattern Recognition 12 (1980), 75–82 DOI 10.1016/0031-3203(80)90005-9 | MR 0567072 | Zbl 0428.68092
[12] Tomek I.: Two modifications of CNN. IEEE Trans. Systems Man Cybernet. 6 (1976), 11, 769–772 DOI 10.1109/TSMC.1976.4309452 | MR 0449068 | Zbl 0341.68066
[13] Yamanishi K.: A learning criterion for stochastic rules. In: Machine Learning, to appear. An extended abstract in Proceedings of the Third Annual Workshop on Computational Learning Theory, 1990, pp. 67–81
Search
Partner of
