#11




Re: Classifying Handwritten Digits: 1 vs. 5
This thread has a response that might help:
http://book.caltech.edu/bookforum/showthread.php?t=2101 Quote:
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#12




Re: Classifying Handwritten Digits: 1 vs. 5
Hello Professor Magdon,
I have a slightly different question related to plotting. When I am asked to "familiarize yourself with the data by giving a plot of two of the digit images", what do we mean by plotting the data? Are we referring to generating a digit image from the greyscale value vector? Or are you referring to somehow plotting the numerical values in the vector, itself? I have a feeling that it is the former, but I am not familar with image generation from greyscale pixels. Any guidance you could provide would be greatly appreciated. 
#13




Re: Classifying Handwritten Digits: 1 vs. 5
If you go to www.amlbook.com, click on 'supporting material' on the right and then scroll down to the `Data' section, you will find some information that can be of use. In particular, there is matlab code for plotting the digit images which takes the matrix of grayscale values and plots an image. This can be of help for developing your own code and utilities.
Quote:
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#14




Re: Classifying Handwritten Digits: 1 vs. 5
Thank you for your assistance. That helps me greatly.

#15




Re: Classifying Handwritten Digits: 1 vs. 5
Quote:

#16




Re: Classifying Handwritten Digits: 1 vs. 5
You can use the weights produced by logistic regression for classification.
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#17




Re: Classifying Handwritten Digits: 1 vs. 5
Hello. I have a question about the digits assignment. Are we supposed to use two
features separately {1, feature i} or have a input vector consisting of {1,feature 1, feature 2}? 
#18




Re: Classifying Handwritten Digits: 1 vs. 5
Thanks for your reply. In the logistic regression will the separator still be linear or not? And should we use fixed or variable step size in the logistic regression?

#19




Re: Classifying Handwritten Digits: 1 vs. 5
{1,feature 1, feature 2}
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#20




Re: Classifying Handwritten Digits: 1 vs. 5
The classification function is still which is linear. The weights are obtained using logistic regression.
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