Optical Recognition of Handwritten Digits of Frank A, Asuncion A (2010).
The dataset describes n = 1797 digits from 0 to 9 (K = 10), handwritten by 13 subjects. Raw observations are 32x32 bitmaps, which are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates p = 64 (= 8x8) variable, recording the normalized counts of pixels in each block and each element is an integer in the range 0 to 16. The row names of the matrix optd contains the true labels (between 0 and 9), and the column names of it contains the position of the block in original bitmap.
A vector containing integers between 1 and 1797.
Given the observation indices, the
The original dataset is freely available from USIMachine Learning Repository (Frank and Asuncion (2010)) website http://archive .ics.uci.edu/ml/datasets.html.
Yumi Kondo <firstname.lastname@example.org>
Frank A, Asuncion A (2010). UCI Machine Learning Repository." http://archive.ics.uci.edu/ml.
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## Not run: data(optd) truedigit <- rownames(optd) (re <- RSKC(optd,ncl=10,alpha=0.1,L1=5.7,nstart=1000)) Sensitivity(re$labels,truedigit) table(re$labels,truedigit) ## Check the bitmap of the trimmed observations showbitmap(re$oW) ## Check the features which receive zero weights names(which(re$weights==0)) ## End(Not run)
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