Critique 2

Comments

1. Overall, a good high-level description of what the algorithm does; comparisons with other methods like least-squares regression and Perceptron are helpful and the article is generally easy to follow. It would be nicer if there were more explanations on how the SVM works, by addressing the following questions:
   1. How do you select the "support vectors"?
   2. How do you actually locate the hyperplane? Is there some kind of iterative method you have to apply?
   3. How do you guarantee convergence?
2. There is a brief description of using the "kernel trick" to extend the application of SVM to non-linear tasks (combining multiple SVMs), but it should be elaborated further as what the "kernel trick" actually does is still unclear. i.e. how do you combine multiple SVMs?
3. Second and Third paragraph of "Builds on" section could be moved to the "Motivation" section. Mentioning the limitations of least-squares approach and Perceptron would motivate the concept of the SVM. The first paragraph about binary classifier may be enough for the "Builds on" section.

Minor Comment

The diagram should be resized to fit the width of the screen; on 1366x768 resolution the last graph (soft-margin SVM) is being clipped

Scheme

• The topic is relevant for the course. 5
• The writing is clear and the English is good. 5
• The page is written at an appropriate level for CPSC 522 students (where the students have diverse backgrounds). 5
• The formalism (definitions, mathematics) was well chosen to make the page easier to understand. 2
• The abstract is a concise and clear summary. 4
• There were appropriate (original) examples that helped make the topic clear. 4
• There was appropriate use of (pseudo-) code. 1
• It had a good coverage of representations, semantics, inference and learning (as appropriate for the topic). 3
• It is correct. 5
• It was neither too short nor too long for the topic. 3
• It was an appropriate unit for a page (it shouldn't be split into different topics or merged with another page). 5
• It links to appropriate other pages in the wiki. 3
• The references and links to external pages are well chosen. 3
• I would recommend this page to someone who wanted to find out about the topic. 5
• This page should be highlighted as an exemplary page for others to emulate. 4
• If I was grading it out of 20, I would give it: 16

Thanks for the tips! I've redone the images since you wrote this, and I'm working on expanding the explanations. I think you're right in that it's worth mentioning how a solution is reached (fairly generic minimization/optimization routines); I'll talk about some of the methods typically applied. I for sure need to cover the kernel trick, it's one of the things that makes modern SVMs so powerful.