论文信息 - Metrics and Models for Handwritten Character Recognition

Metrics and Models for Handwritten Character Recognition

Figure 1 shows some handwritten digits taken from US envelopes. Each image consists of 16 × 16 pixels of greyscale values ranging from 0 – 255. These 256 pixel values are regarded as a feature vector to be used as input to a classifier, which will automatically assign a digit class based on the pixel values.

T. Hastie | P. Simard

[1] J. Friedman,et al. Projection Pursuit Regression , 1981 .

[2] Teuvo Kohonen,et al. Self-Organization and Associative Memory , 1988 .

[3] Teuvo Kohonen,et al. Self-organization and associative memory: 3rd edition , 1989 .

[4] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..

[5] Lawrence D. Jackel,et al. Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[6] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.

[7] Yann LeCun,et al. Efficient Pattern Recognition Using a New Transformation Distance , 1992, NIPS.

[8] Eric Mjolsness,et al. Clustering with a Domain-Specific Distance Measure , 1993, NIPS.

[9] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[10] R. Tibshirani,et al. Flexible Discriminant Analysis by Optimal Scoring , 1994 .

[11] E. Mjolsness,et al. Clustering with a Domain-speciic Distance Measure , 1994 .

[12] Trevor Hastie,et al. Handwritten Digit Recognition via Deformable Prototypes , 1994 .

[13] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .

[14] Yoshua Bengio,et al. Pattern Recognition and Neural Networks , 1995 .

[15] R. Tibshirani,et al. Penalized Discriminant Analysis , 1995 .

[16] R. Tibshirani,et al. Discriminant Analysis by Gaussian Mixtures , 1996 .