Section A
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Prob 6:
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Typo: the proof gives an equation for u3. The u_2's in one
term in the numerator and one term in the denominator should be u_1's.
Section B
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Prob 1:
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Q. What is the stopping criterion for the Perceptron
training algorithm?
A. It should stop when it makes no errors at all on the training data. For
purposes of this assignment, you may assume that the training data is
always linearly separable and the Perceptron will always stop, after
enough updates.
Q. Should we allow for offsets (i.e. \theta_0) in the classifier?
A. No. The decision boundary should always go through the origin, i.e.,
\theta_0 = 0 always.
Prob 2:
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Q. I've implemented the SVM in matlab, but it seems to be
taking a really long time to train the data from Prob #1. Is this normal,
or am I doing something wrong?
A. We just did a quick svm_train() implementation and it seems to run
fairly quickly (i.e. in seconds) on data from Prob #1. It also seem to be
giving the right results as well, so I don't know what's going wrong for
you. Try the MATLAB on Athena, if you aren't sure your version is the
latest and greatest.
Q. When we implement SVM classifier, do we need to include 'c' as one of
the input?
A. No, you needn't.
Q. Should we use the generalized rule with the offset etc.?
A. No, you needn't. For this problem, just implement a simple SVM without
offset or regularization.
Prob 3:
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Q. In part (a) we are asked to plot the image which was
mis-classified. Could you tell me how I should do that? I looked into the
data file, and it seems to be some integer values. But how can I plot
them?
A. look at the strimage.m code in the "data" section of the pset:
http://courses.csail.mit.edu/6.867/hw1/
Also look at the link to actual PNG images (also available from pset
page): http://alawi.csail.mit.edu/~alawi/6867/hw1/images/