pendigits.tes: Pen-Based Recognition of Handwritten Digits Data Set (testing...
In nsdr: Nonlinear Sufficient Dimension Reduction
Description
Usage
Format
Source
References
Description
The data is about the recognition of handwritten numbers from 0 to 9.
There are 30 writers in the training dataset and each participant are asked to write 250 digits in random order.
Without missing data, this dataset has 7494 observations.
The experiment uses WACOM tablet, which has 500 x 500 pixel resolutions and normalized it to a maximum scale of 100.
The researcher considers spatial resampling.
Thus, for each digit, eight pairs of 2 dimensional (x axis and y axis) locations are recorded, which makes this dataset have 16 dimensional predictor variables.
Usage
1
Format
A data frame with 2219 observations on 17 variables.
The first column to the 16th column represent resampled values of the pairs of points.
The 17th column presents the digit (0 to 9).
Source
https://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits
References
F. Alimoglu (1996) Combining Multiple Classifiers for Pen-Based Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University.
F. Alimoglu, E. Alpaydin, "Methods of Combining Multiple Classifiers Based on Different Representations for Pen-based Handwriting Recognition," Proceedings of the Fifth Turkish Artificial Intelligence and Artificial Neural Networks Symposium (TAINN 96), June 1996, Istanbul, Turkey.
nsdr documentation built on June 3, 2021, 9:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Format Source References
Description
The data is about the recognition of handwritten numbers from 0 to 9. There are 30 writers in the training dataset and each participant are asked to write 250 digits in random order. Without missing data, this dataset has 7494 observations. The experiment uses WACOM tablet, which has 500 x 500 pixel resolutions and normalized it to a maximum scale of 100. The researcher considers spatial resampling. Thus, for each digit, eight pairs of 2 dimensional (x axis and y axis) locations are recorded, which makes this dataset have 16 dimensional predictor variables.
Usage
1 |
Format
A data frame with 2219 observations on 17 variables.
The first column to the 16th column represent resampled values of the pairs of points. The 17th column presents the digit (0 to 9).
Source
https://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits
References
F. Alimoglu (1996) Combining Multiple Classifiers for Pen-Based Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University. F. Alimoglu, E. Alpaydin, "Methods of Combining Multiple Classifiers Based on Different Representations for Pen-based Handwriting Recognition," Proceedings of the Fifth Turkish Artificial Intelligence and Artificial Neural Networks Symposium (TAINN 96), June 1996, Istanbul, Turkey.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.