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diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/pendigits.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/pendigits.names new file mode 100644 index 00000000..2d8a87f7 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/pendigits.names @@ -0,0 +1,136 @@ +1. Title of Database: Pen-Based Recognition of Handwritten Digits + +2. Source: + E. Alpaydin, Fevzi. Alimoglu + Department of Computer Engineering + Bogazici University, 80815 Istanbul Turkey + alpaydin@boun.edu.tr + July 1998 + +3. Past Usage: + F. Alimoglu (1996) Combining Multiple Classifiers for Pen-Based + Handwritten Digit Recognition, + MSc Thesis, Institute of Graduate Studies in Science and + Engineering, Bogazici University. + http://www.cmpe.boun.edu.tr/~alimoglu/alimoglu.ps.gz + + 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. + http://www.cmpe.boun.edu.tr/~alimoglu/tainn96.ps.gz + + +4. Relevant Information: + + We create a digit database by collecting 250 samples from 44 writers. + The samples written by 30 writers are used for training, + cross-validation and writer dependent testing, and the digits + written by the other 14 are used for writer independent testing. This + database is also available in the UNIPEN format. + + We use a WACOM PL-100V pressure sensitive tablet with an integrated + LCD display and a cordless stylus. The input and display areas are + located in the same place. Attached to the serial port of an Intel + 486 based PC, it allows us to collect handwriting samples. The tablet + sends $x$ and $y$ tablet coordinates and pressure level values of the + pen at fixed time intervals (sampling rate) of 100 miliseconds. + + These writers are asked to write 250 digits in random order inside + boxes of 500 by 500 tablet pixel resolution. Subject are monitored + only during the first entry screens. Each screen contains five boxes + with the digits to be written displayed above. Subjects are told to + write only inside these boxes. If they make a mistake or are unhappy + with their writing, they are instructed to clear the content of a box + by using an on-screen button. The first ten digits are ignored + because most writers are not familiar with this type of input devices, + but subjects are not aware of this. + + In our study, we use only ($x, y$) coordinate information. The stylus + pressure level values are ignored. First we apply normalization to + make our representation invariant to translations and scale + distortions. The raw data that we capture from the tablet consist of + integer values between 0 and 500 (tablet input box resolution). The + new coordinates are such that the coordinate which has the maximum + range varies between 0 and 100. Usually $x$ stays in this range, since + most characters are taller than they are wide. + + In order to train and test our classifiers, we need to represent + digits as constant length feature vectors. A commonly used technique + leading to good results is resampling the ( x_t, y_t) points. + Temporal resampling (points regularly spaced in time) or spatial + resampling (points regularly spaced in arc length) can be used here. + Raw point data are already regularly spaced in time but the distance + between them is variable. Previous research showed that spatial + resampling to obtain a constant number of regularly spaced points + on the trajectory yields much better performance, because it provides + a better alignment between points. Our resampling algorithm uses + simple linear interpolation between pairs of points. The resampled + digits are represented as a sequence of T points ( x_t, y_t )_{t=1}^T, + regularly spaced in arc length, as opposed to the input sequence, + which is regularly spaced in time. + + So, the input vector size is 2*T, two times the number of points + resampled. We considered spatial resampling to T=8,12,16 points in our + experiments and found that T=8 gave the best trade-off between + accuracy and complexity. + + +5. Number of Instances + pendigits.tra Training 7494 + pendigits.tes Testing 3498 + + The way we used the dataset was to use first half of training for + actual training, one-fourth for validation and one-fourth + for writer-dependent testing. The test set was used for + writer-independent testing and is the actual quality measure. + +6. Number of Attributes + 16 input+1 class attribute + +7. For Each Attribute: + All input attributes are integers in the range 0..100. + The last attribute is the class code 0..9 + +8. Missing Attribute Values + None + +9. Class Distribution + Class: No of examples in training set + 0: 780 + 1: 779 + 2: 780 + 3: 719 + 4: 780 + 5: 720 + 6: 720 + 7: 778 + 8: 719 + 9: 719 + Class: No of examples in testing set + 0: 363 + 1: 364 + 2: 364 + 3: 336 + 4: 364 + 5: 335 + 6: 336 + 7: 364 + 8: 336 + 9: 336 + +Accuracy on the testing set with k-nn +using Euclidean distance as the metric + + k = 1 : 97.74 + k = 2 : 97.37 + k = 3 : 97.80 + k = 4 : 97.66 + k = 5 : 97.60 + k = 6 : 97.57 + k = 7 : 97.54 + k = 8 : 97.54 + k = 9 : 97.46 + k = 10 : 97.48 + k = 11 : 97.34 |