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assignments:assignment4 [2016/09/30 10:22] asa [Grading] |
assignments:assignment4 [2016/09/30 10:24] asa |
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Due: October 17th at 11:59pm | Due: October 17th at 11:59pm | ||
- | ===== Part 1: SVM with no bias term ===== | + | ==== Part 1: SVM with no bias term ==== |
Formulate a soft-margin SVM without the bias term, i.e. one where the discriminant function is equal to $\mathbf{w}^{T} \mathbf{x}$. | Formulate a soft-margin SVM without the bias term, i.e. one where the discriminant function is equal to $\mathbf{w}^{T} \mathbf{x}$. | ||
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- | ===== Part 3: Soft-margin SVM for separable data ===== | + | ==== Part 3: Soft-margin SVM for separable data ==== |
Suppose you are given a linearly separable dataset, and you are training the soft-margin SVM, which uses slack variables with the soft-margin constant $C$ set | Suppose you are given a linearly separable dataset, and you are training the soft-margin SVM, which uses slack variables with the soft-margin constant $C$ set | ||
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Is this true or false? Explain! | Is this true or false? Explain! | ||
- | ===== Part 4: Using SVMs ===== | + | ==== 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 | ||
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* The report is well structured, the writing is clear, with good grammar and correct spelling; good formatting of math, code, figures and captions (every figure and table needs to have a caption that explains what is being shown). | * The report is well structured, the writing is clear, with good grammar and correct spelling; good formatting of math, code, figures and captions (every figure and table needs to have a caption that explains what is being shown). | ||
* Whenever you use information from the web or published papers, a reference should be provided. Failure to do so is considered plagiarism. | * Whenever you use information from the web or published papers, a reference should be provided. Failure to do so is considered plagiarism. | ||
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- | We will take off points if these guidelines are not followed. | ||
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* 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. | * 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. | ||
* 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. | * 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. | * 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. | ||
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+ | We will take off points if these guidelines are not followed. | ||
<code> | <code> |