Machine Learning Homework 4 solved
1 Support Vector Machines (50 pts)
In this homework you’ll explore the primal and dual representations of support vector machines,
as well as explore the performance of various kernels while classifying handwritten digits.
1.1 Programming questions (20 pts)
1. Given a weight vector, implement the find support function that returns the indices of the
2. Given a weight vector, implement the find slack function that returns the indices of the vectors
with nonzero slack.
3. Given the alpha dual vector, implement the weight vector function that returns the corresponding weight vector.
1.2 Analysis (30 pts)
Use svm fours nines.py to help answer the analysis questions.
Please do NOT submit svm fours nines.py to Moodle.
This file is to just help read in data and run the GridSearch.
1. Use the Sklearn implementation of support vector machines to train a classifier to distinguish
4’s from 9’s (using the MNIST data from the KNN homework).
2. Experiment with linear, polynomial, and RBF kernels. In each case, perform a GridSearch
to help determine optimal hyperparameters for the given model (e.g. C for linear kernel, C
and p for polynomial kernel, and C and γ for RBF). Comment on the experiments you ran
and optimal hyperparameters you found.
Hint: http://scikit-learn.org/stable/modules/grid search.html
3. Comment on classification performance for each model for optimal parameters by either
testing on a hold-out set or performing cross-validation.
Homework 4 CSCI 5622
4. Give examples (in picture form) of support vectors from each class when using a polynomial
2 Learnability (25 pts)
Consider the class C of concepts defined by triangles with distinct vertices of the form (i, j) where
i and j are integers in the interval [0, 99]. A concept c labels points on the interior and boundary
of a triangle as positive and points on the exterior of the triangle as negative.
Give a bound on the number of randomly drawn training examples sufficient to assure that for
any target class c in C, any consistent learner will, with probability 95%, output a hypothesis with
error at most 0.15.
Note: To make life easier, we’ll allow degenerate triangles in C. That is, triangles where the
vertices are collinear. The following image depicts an example of a degenerate and non-degenerate
3 VC Dimension (25 pts)
This questions concerns feature vectors in two-dimensional space. Consider the class of hypotheses
defined by circles centered at the origin. A hypothesis h in this class can either classify points as
positive if they lie on the boundary or interior of the circle, or can classify points as positive if they
lie on the boundary or exterior of the circle. State and prove (rigorously) the VC dimension of this
Homework 4 CSCI 5622
family of classifiers.
EXTRA CREDIT (10 pts): Consider the class of hypotheses defined by circles anywhere in 2D
space. A hypothesis h in this class will classify points as positive if they lie on the boundary or
interior of the circle, and classify points as negative if they lie on the exterior of the circle. State
and prove (rigorously) the VC dimension of this family of classifiers.
You'll get 1 file (277.4KB)