assignments:assignment3
Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
assignments:assignment3 [2013/10/06 13:33] asa |
assignments:assignment3 [2013/10/09 12:19] asa |
||
|---|---|---|---|
| Line 1: | Line 1: | ||
| ========= Assignment 3: Support Vector Machines ============ | ========= Assignment 3: Support Vector Machines ============ | ||
| + | |||
| + | Due: October 20th at 6pm | ||
| ===== Part 1: SVM with no bias term ===== | ===== Part 1: SVM with no bias term ===== | ||
| Line 14: | Line 16: | ||
| Express the closest centroid algorithm in terms of kernels, i.e. determine how the coefficients $\alpha_i$ will be computed using a given labeled dataset. | Express the closest centroid algorithm in terms of kernels, i.e. determine how the coefficients $\alpha_i$ will be computed using a given labeled dataset. | ||
| - | ===== Part 3: Using SVMs ===== | + | ===== Part 3: Soft-margin SVM for separable data ===== |
| + | |||
| + | Consider training a soft-margin SVM | ||
| + | with $C$ set to some positive constant. Suppose the training data is linearly separable. | ||
| + | Since increasing the $\xi_i$ can only increase the objective of the primal problem (which | ||
| + | we are trying to minimize), at the optimal solution to the primal problem, all the | ||
| + | training examples will have $\xi_i$ equal | ||
| + | to zero. True or false? Explain! | ||
| + | Given a linearly separable dataset, is it necessarily better to use a | ||
| + | a hard margin SVM over a soft-margin SVM? | ||
| + | |||
| + | ===== Part 4: Using SVMs ===== | ||
| The data for this question comes from a database called SCOP (structural | The data for this question comes from a database called SCOP (structural | ||
| Line 64: | Line 77: | ||
| K_{poly}(\mathbf{x}, \mathbf{x'}) = (1 + \mathbf{x}^T \mathbf{x}') ^{p} | K_{poly}(\mathbf{x}, \mathbf{x'}) = (1 + \mathbf{x}^T \mathbf{x}') ^{p} | ||
| $$ | $$ | ||
| - | Plot the accuracy of the SVM, measured using the balnced success rate | + | Plot the accuracy of the SVM, measured using the balanced success rate |
| as a function of both the soft-margin parameter of the SVM, and the free parameter | as a function of both the soft-margin parameter of the SVM, and the free parameter | ||
| of the kernel function. | of the kernel function. | ||
| Line 91: | Line 104: | ||
| Explain the structure that you are seeing in the plot (it is more | Explain the structure that you are seeing in the plot (it is more | ||
| interesting when the data is normalized). | interesting when the data is normalized). | ||
| + | |||
| + | ===== Submission ===== | ||
| + | |||
| + | Submit your report via RamCT. Python code can be displayed in your report if it is succinct (not more than a page or two at the most) or submitted separately. The latex sample document shows how to display Python code in a latex document. | ||
| + | Also, please check-in a text file named README that describes what you found most difficult in completing this assignment (or provide that as a comment on ramct). | ||
| + | |||
| + | ===== Grading ===== | ||
| + | |||
| + | Here is what the grade sheet will look like for this assignment. A few general guidelines for this and future assignments in the course: | ||
| + | |||
| + | * Always provide a description of the method you used to produce a given result in sufficient detail such that the reader can reproduce your results on the basis of the description. You can use a few lines of python code or pseudo-code. If your code is more than a few lines, you can include it as an appendix to your report. For example, for the first part of the assignment, provide the protocol you use to evaluate classifier accuracy. | ||
| + | * You can provide results in the form of tables, figures or text - whatever form is most appropriate for a given problem. There are no rules about how much space each answer should take. BUT we will take off points if we have to wade through a lot of redundant data. | ||
| + | * In any machine learning paper there is a discussion of the results. There is a similar expectation from your assignments that you reason about your results. For example, for the learning curve problem, what can you say on the basis of the observed learning curve? | ||
| + | |||
| + | <code> | ||
| + | Grading sheet for assignment 2 | ||
| + | |||
| + | Part 1: 30 points. | ||
| + | (10 points): Lagrangian found correctly | ||
| + | ( 5 points): Derivation of saddle point equations | ||
| + | (10 points): Derivation of the dual | ||
| + | ( 5 points): Discussion of the implication of the form of the dual for SMO-like algorithms | ||
| + | |||
| + | Part 2: 15 points. | ||
| + | |||
| + | Part 3: 15 points. | ||
| + | |||
| + | Part 1: 40 points. | ||
| + | (25 points): Accuracy as a function of parameters and discussion of the results | ||
| + | (10 points): Comparison of normalized and non-normalized results | ||
| + | ( 5 points): Visualization of the kernel matrix and observations made about it | ||
| + | |||
| + | Report structure, grammar and spelling: 15 points | ||
| + | ( 5 points): Heading and subheading structure easy to follow and | ||
| + | clearly divides report into logical sections. | ||
| + | ( 5 points): Code, math, figure captions, and all other aspects of | ||
| + | report are well-written and formatted. | ||
| + | ( 5 points): Grammar, spelling, and punctuation. | ||
| + | </code> | ||
assignments/assignment3.txt · Last modified: 2016/09/20 09:34 by asa
Page Tools
Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Share Alike 4.0 International
