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The addition of an extra term, called a proportional factor reduces the convergence of the back propagation algorithm.
So how to avoid local minima in Back propagation algorithm.
In local minimum a gradient of an error function is a zero vector - so backprop - which is using a gradient - cannot move your parameters any further and finishes training.
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I am working on a small computer vision project and I'm using convolutional nets for classification. I have already used dropout, l1, l2 regularization and data augmentation to reduce overfitting. Are there any other techniques and algorithms for improving model accuracy and reducing overfitting?
there could be a 100 solutions
Use pretrained model (transfer learning).
Try to implement a smaller network.
Bigger data set.
Try different parameters[learning rate, batch size..].
Use grid search for these parameters.
Try data augmentation for your training data set.
...
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How to calculate the energy of the function tri(2t+5/6) using matlab?[where tri is triangular pulse function]
Define trisqr square function and integrate over it:
trisqr=#(x) tri(x).*tri(x);
xmin=-2;
xmax=2;
Energy=integral(trisqr,xmin,xmax)
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How can I detect P-R interval from an ECG signal using MATLAB ? Can anyone give me code or any steps to determine this ?
Find the P wave (s).
Find the QRS complex(es).
Pair up a QRS complex and its preceding P wave. Make sure to avoid cases where there is a PVC or other abnormal beat in between.
Subtract the time of the P wave from the time of the R peak in the QRS complex.
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I want to use genetic algorithm in matlab. I have a matrix as a population.
how can I use a specific matrix as a population to input in ga function of Matlab?
use gaoptimset setting the 'InitialPopulation' field and pass the result to your ga as the options argument
example:
pop = {rand(n,m)} %// The {} might not be necessary.
options = gaoptimset('InitialPopulation',pop);
x = ga(....., options)
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I have divided an image into 30 blocks and I want to apply radon transform on each block but I do not know how to do that. I know the radon function in matlab but it is for whole image not for each block.
How may I use the radon function for image blocks?
If you already have the image split into blocks, just call radon() for each block.
You could use blockproc, if you have image toolbox.