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 [2015/10/02 12:05] asa |
assignments:assignment3 [2015/10/02 12:12] asa |
||
|---|---|---|---|
| Line 1: | Line 1: | ||
| ========= Assignment 3: Support Vector Machines ============ | ========= Assignment 3: Support Vector Machines ============ | ||
| - | Due: October 20th at 6pm | + | Due: October 16th at 11pm |
| ===== Part 1: SVM with no bias term ===== | ===== Part 1: SVM with no bias term ===== | ||
| Line 45: | Line 45: | ||
| d1scta_,a.1.1.2 31417:1.0 32645:1.0 39208:1.0 42164:1.0 .... | d1scta_,a.1.1.2 31417:1.0 32645:1.0 39208:1.0 42164:1.0 .... | ||
| </code> | </code> | ||
| - | The first column is the ID of the protein, the second is the class it belongs to (the values for the class variable are ''a.1.1.2'', which is the given class of proteins, and ''rest'' which is the negative class representing the rest of the database), and the rest of the elements are pairs of the form ''feature_id:value'' - an id of a feature and the value associated with it. | + | The first column is the ID of the protein, the second is the class it belongs to (the values for the class variable are ''a.1.1.2'', which is the given class of proteins, and ''rest'' which is the negative class representing the rest of the database); the remainder consists of elements of the form ''feature_id:value''which provide an id of a feature and the value associated with it. |
| This is an extension of the format used by LibSVM, that scikit-learn can read. | This is an extension of the format used by LibSVM, that scikit-learn can read. | ||
| - | See a discussion [[http://scikit-learn.org/stable/datasets/#datasets-in-svmlight-libsvm-format | here]]. | + | See a discussion of this format and how to read it [[http://scikit-learn.org/stable/datasets/#datasets-in-svmlight-libsvm-format | here]]. |
| We note that the data is very high dimensional since | We note that the data is very high dimensional since | ||
| Line 64: | Line 64: | ||
| K_{gauss}(\mathbf{x}, \mathbf{x'}) = \exp(-\gamma || \mathbf{x} - \mathbf{x}' ||^2) | K_{gauss}(\mathbf{x}, \mathbf{x'}) = \exp(-\gamma || \mathbf{x} - \mathbf{x}' ||^2) | ||
| $$ | $$ | ||
| + | and | ||
| $$ | $$ | ||
| - | K_{poly}(\mathbf{x}, \mathbf{x'}) = (\mathbf{x}^T \mathbf{x}' + 1) ^{p} | + | K_{poly}(\mathbf{x}, \mathbf{x'}) = (\mathbf{x}^T \mathbf{x}' + 1) ^{p}. |
| $$ | $$ | ||
| Line 94: | Line 95: | ||
| ===== Submission ===== | ===== Submission ===== | ||
| - | Submit your report via Canvas. 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. Code needs to be there so we can make sure that you implemented the algorithms and data analysis methodology correctly. Canvas allows you to submit multiple files for an assignment, so DO NOT submit an archive file (tar, zip, etc). | + | Submit the pdf of your report via Canvas. 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. Code needs to be there so we can make sure that you implemented the algorithms and data analysis methodology correctly. Canvas allows you to submit multiple files for an assignment, so DO NOT submit an archive file (tar, zip, etc). Canvas will only allow you to submit pdfs (.pdf extension) or python code (.py extension) |
| ===== Grading ===== | ===== Grading ===== | ||
| Line 108: | Line 109: | ||
| Grading sheet for assignment 2 | Grading sheet for assignment 2 | ||
| - | Part 1: 45 points. | + | Part 1: 40 points. |
| (10 points): Primal SVM formulation is correct | (10 points): Primal SVM formulation is correct | ||
| (10 points): Lagrangian found correctly | (10 points): Lagrangian found correctly | ||
| Line 115: | Line 116: | ||
| ( 5 points): Discussion of the implication of the form of the dual for SMO-like algorithms | ( 5 points): Discussion of the implication of the form of the dual for SMO-like algorithms | ||
| - | Part 2: 15 points. | + | Part 2: 10 points. |
| Part 3: 40 points. | Part 3: 40 points. | ||
assignments/assignment3.txt ยท 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
