Hello,
I want to make a feedforward backpropagation neural network in order to solve a classification problem.
Let's say I want to import a data set from UCI Machine Learning Repository ( this one ) which is 4x306. How do I create a Target data set in order to train it?
ANSWER
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The target for 2-class classification has dimensions [1 N] (N=306) with values {0,1}. However, if the ratio of N1/N0 is not in the interval [0.5, 2 ], then randomly add duplicates of the smaller class until the class sizes are equal. Occasionally, it helps to add a small noise component to the duplicates. Use PATTERNNET with a LOGSIG activation function and TRAINSCG training function. The corresponding output, y = net(x), is a consistent estimate of the posterior probability of the "1" class, given the input x, i.e., P(classind = 1 | x ). the corresponding class index can be obtained from round(y).
Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.
The target for 2-class classification has dimensions [1 N] (N=306) with values {0,1}. However, if the ratio of N1/N0 is not in the interval [0.5, 2 ], then randomly add duplicates of the smaller class until the class sizes are equal. Occasionally, it helps to add a small noise component to the duplicates.
Use PATTERNNET with a LOGSIG activation function and TRAINSCG training function. The corresponding output, y = net(x), is a consistent estimate of the posterior probability of the "1" class, given the input x, i.e., P(classind = 1 | x ). the corresponding class index can be obtained from round(y).
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