Pattern recognition Design a pattern recognition neural network to recognize handwriting digits (0 9) For example, the following sample represents a handwriting 9 00000000000000000000000000000000 00000000000001111100000000000000 00000000000001111100000000000000 00000000001111111110000000000000 00000000011111111111111000000000 00000000011111111111111100000000 00000000111111111111111100000000 00000000111111111111111110000000 00000001111111100001111110000000 00000001111111000001111111000000 00000001111110000001111111000000 00000001111110000000111111000000 00000011111100000001111110000000 00000001111111000001111110000000 00000001111111110001111110000000 00000001111111111111111110000000 00000000001111111111111110000000 00000000001111111111111110000000 00000000000111111111111110000000 00000000000000111001111110000000 00000000000000000000111111000000 00000000000000000000111100000000 00000000000000000000111100000000 00000000000000000000111100000000 00000000000000000001111110000000 00000000000000000001111110000000 00000000001111100001111100000000 00000000001111111111111100000000 00000000001111111111111100000000 00000000011111111111111000000000 00000000000011111111111000000000 00000000000000001111110000000000 The data file can be downloaded from https archive ics uci edu ml machine learning databases optdigits optdigits orig windep Z The data file contains 1797 instances from 43 writers The data is prepared by NIST to extract normalized bitmaps of handwritten digits from a preprinted form Task 1 Encoding Unzip the data file from the link Encode the data into input and target files for neural network training Task 2 Neural Network Training Train the neural network (pattern net) based on your input output files Repeat with 10, 100, and 500 hidden nodes Report your testing, training, and validation accuracy and provide analysis Task 3 Using 1024 features for neural network is very cumbersome Is there a way to reduce the number of features

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Pattern recognition Design a pattern recognition neural network to recognize handwriting digits (0-9). For example, the following sample represents a handwriting 9. 00000000000000000000000000000000 00000000000001111100000000000000 00000000000001111100000000000000

Pattern recognition

Design a pattern recognition neural network to recognize handwriting digits (0-9). For example, the following sample represents a handwriting 9.

00000000000000000000000000000000

00000000000001111100000000000000

00000000001111111110000000000000

00000000011111111111111000000000

00000000011111111111111100000000

00000000111111111111111100000000

00000000111111111111111110000000

00000001111111100001111110000000

00000001111111000001111111000000

00000001111110000001111111000000

00000001111110000000111111000000

00000011111100000001111110000000

00000001111111000001111110000000

00000001111111110001111110000000

00000001111111111111111110000000

00000000001111111111111110000000

00000000000111111111111110000000

00000000000000111001111110000000

00000000000000000000111111000000

00000000000000000000111100000000

00000000000000000001111110000000

00000000001111100001111100000000

00000000001111111111111100000000

00000000011111111111111000000000

00000000000011111111111000000000

00000000000000001111110000000000

The data file can be downloaded from

https://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/optdigits-orig.windep.Z

The data file contains 1797 instances from 43 writers. The data is prepared by NIST to extract normalized bitmaps of handwritten digits from a preprinted form.

Task 1: Encoding

Unzip the data file from the link. Encode the data into input and target files for neural network training.

Task 2: Neural Network Training

Train the neural network (pattern net) based on your input/output files. Repeat with 10, 100, and 500 hidden nodes. Report your testing, training, and validation accuracy and provide analysis.

Task 3:

Using 1024 features for neural network is very cumbersome. Is there a way to reduce the number of features?