Run the Gaussian kernel script from Octave: run gaussian.m.

Re-run it and report the new parameters and values you got in your homework.

Use a plain text editor (TextEdit, Vim editor, Notepad, etc.) to change the coef0 and gamma variables (try non-negative real numbers) in the sigmoid.m script.

Run the Sigmoid kernel script from Octave: octave sigmoid.m.

Unzip them and move to the created directory, then launch Octave:

Download the exercise files and move them to the working directory of your Terminal/Cygwin Shell (type pwd to find your current working directory).

Try out Shogun/Octave on your personal computer (after following step 2 for your OS of choice).

By now you should see that finding the 'just right' rules to make a decision bounary is difficult to do by hand.Can you draw a line to separate the two classes of red and blue? Open '2d 100x100' then the worksheet '2d 10x10'. This represents the prediction scores from columns x1 and x2 for each data point (slightly zoomed). Can you pick a horizontal line to separate the blue and red data? Now look at the further zoomed '1d +/-10' worksheet. The two classes are shown in red and blue. This is zoomed (close up) data for the prediction scores of the x1 column. What is your conclusion? How did it change as you added more data? Was it difficult to figure out? Using the x1, what do you predict point 1 to be? Now try using x1 and x2. The class or label on the far right describes the true classification (-1 or +1 for introns and exons respectively). Positive scores are prediction to be exonic while negative scores are predicted to be intronic for the respective column's gene prediction scheme. Download the example excel file for the SVM training data.Binary versions (pre-compliled) of Octave are available from Octave-Forge, but one must still compile Shogun and install it (you are on your own if you go this route).

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