Constantinos Panagiotakopoulos

I am a Professor at the School of Technology of the Aristotle University of Thessaloniki.
Papers concerning my research in Theoretical High Energy Physics and Cosmology can be found here .
My research in Machine Learning is the following.
Margin Perceptron with Unlearning
Perceptron with Dynamic Margin
Perceptron with Weight Shrinking
Stochastic Gradient Descent


In the above programs the seed of the random number generator was fixed to 0 which was the default value of previous Cygwin releases.


The programs compile with the g++ compiler.
In order to make the .exe under Cygwin type the command:
$ g++ -Wall -lm -O3 -o train
To extend the maximum amount of allocatable memory set the desirable size in the .exe file. E.g., for a size of 1024 MB the command is
$ peflags --cygwin-heap=1024 train.exe
For the .exe files given the heap size was set to 1024. To run the .exe files on Windows platform one needs cygwin1.dll which comes with the Cygwin setup.
To see the available inputs for each program write
$ ./train
To run the program write
$ ./train [inputs] datafile modelfile
The datafile should be given in SVM-Light format. This means that each example takes up one line. The label is from the set {-1,+1} and comes first. Then only the attributes with non-zero value should be provided separated from their value by the character ':'
A typical line reads like this:
-1 1:2.11 3:4.01 7:9.0 15:2.5
If the user doesn't provide a name for a modelfile one will be created in the form datafile.model. The modelfile contains the components of the produced augmented weight vector. Especially, for the perceptron with unlearning solving for L1-soft margin the weight vector a is divided by b since w=a/b . Always the first component corresponds to the extra feature rho of the augmented patterns.


For any question regarding the papers or the programs feel free to contact either of the authors.