implement the ANN-based classifier for three class of vehicles-train, bus and two wheeler
1 view (last 30 days)
Show older comments
The first three columns correspond to the output label-the type of vehicle chosen by a person for office travel. remaining columns are the input characteristics based on the vehicle mode that is chosen. Use only selected input characteristics for classifier. Here is procedure to follow:
1. select 150 rows (4-10)columns from the text and read,normalize and input the data for training
2. selct 150 rows and (1-3) colums and read as data for train output(train data labels)
3.select (151-182)rows and (4-10)colums from the text and read,normalize and input data for test
4. convert the obtained output from neural network in to a form similar to train data labels to produce output labels
5. Compute the accuracy of classifier in %
0 Comments
Accepted Answer
Greg Heath
on 6 May 2013
%1. select 150 rows (4-10)columns from the text and read,normalize and input the data for training.
Then transpose to obtain
[ I Ntrn ] = size(xtrn) % % I = 7, Ntrn = 150
%2. selct 150 rows and (1-3) colums and read as data for train output(train data labels)
Convert the target code to rows of the eye(3). Then transpose to obtain
[ O Ntrn ] = size(ttrn) % % O = 3, Ntrn = 150
% Train a classifier
help patternnet
doc patternnet
%3.select (151-182)rows and (4-10)colums from the text and read,normalize and input data for test
[ I Ntst ] = size(xtst) % I = 7, Ntst = 32
ytst = vec2ind(net(xtst));
4. convert the obtained output from neural network in to a form similar to train data labels to produce output labels
5. Compute the accuracy of classifier in %
Hope this helps.
Thank you for formally accepting my answer
Greg
0 Comments
More Answers (1)
Image Analyst
on 5 May 2013
Which step(s) of your homework does your professor want us to do for you? Which do you want us to do for you, or which do you want hints/guidance on?
0 Comments
See Also
Categories
Find more on Sequence and Numeric Feature Data Workflows in Help Center and File Exchange
Products
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!