Description

We used preprocessing programs made available by NIST to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions. For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994.

Related Papers

  • Erick Cant-Paz and Chandrika Kamath. Using Evolutionary Algorithms to Induce Oblique Decision Trees. Center for Applied Scientific Computing Lawrence Livermore National Laboratory. [link]
  • Ethem Alpaydin. Combined 5 x 2 cv F Test for Comparing Supervised Classification Learning Algorithms. Neural Computation, 11. 1999. [link]
  • Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005. [link]
  • Claudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000. [link]
  • Stephen D. Bay. Nearest neighbor classification from multiple feature subsets. Intell. Data Anal, 3. 1999. [link]
  • Ayhan Demiriz and Kristin P. Bennett and John Shawe and I. Nouretdinov V.. Linear Programming Boosting via Column Generation. Dept. of Decision Sciences and Eng. Systems, Rensselaer Polytechnic Institute. [link]
  • [link]
  • [link]
  • [link]

Related datasets