Abstract
We present recent improvements in using subspace classifiers in recognition of handwritten digits. Both non-trainable CLAFIC and trainable ALSM methods are used with four models for initial selection of subspace dimensions and their further error-driven refinement. The results indicate that these additions to the subspace classification scheme noticeably reduce the classification error.
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© 1996 Springer-Verlag Berlin Heidelberg
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Laaksonen, J., Oja, E. (1996). Subspace dimension selection and averaged learning subspace method in handwritten digit classification. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_41
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DOI: https://doi.org/10.1007/3-540-61510-5_41
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