Abstract—A recent practical research reported that a layered
neural network when applies pattern coding, which is a method
to convert analogue input value into a multidimensional binary
pattern, has a high capability of pattern classification. However,
it does not conduct sufficient basic analysis; thus, the effect of
pattern coding is unclear. The present study examines the
effectiveness of pattern coding in pattern classification.
Numerical experiments on two-dimensional two-class
classification problems show that a multilayer perceptron can
learn complex decision boundaries when pattern coding is
applied. The results also indicate that pattern coding is also
effective for a simple perceptron if selective desensitization is
Index Terms—Neural networks, pattern classification, pattern coding, selective desensitization.
T. Tanno, K. Horie, and T. Kobayashi are with the Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan (e-mail: email@example.com, firstname.lastname@example.org, email@example.com).
M. Morita is with the Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan (e-mail: firstname.lastname@example.org).
Cite: Tomohiro Tanno, Kazumasa Horie, Takaaki Kobayashi, and Masahiko Morita, "Effect of Patten Coding on Pattern Classification Neural Networks," International Journal of Machine Learning and Computing vol. 5, no. 4, pp. 339-343, 2015.