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assignments:assignment3 [CS545 fall 2016]

CS545 fall 2016

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CS545


Instructor
Asa Ben-Hur



http://www.colostate.edu

assignments:assignment3

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Assignment 3: Support Vector Machines

Part 1: SVM with no bias term

Formulate a soft-margin SVM without the bias term, i.e. $f(\mathbf{x}) = \mathbf{w}^{T} \mathbf{x}$. Derive the saddle point conditions, KKT conditions and the dual. Compare it to the standard SVM formulation. What is the implication of the difference on the design of SMO-like algorithms? Recall that SMO algorithms work by iteratively optimizing two variables at a time. Hint: consider the difference in the constraints.

Discuss the merit of the bias-less formulation as the dimensionality of the data (or the feature space) is varied. When using this SVM formulation it may be useful to add a constant to the kernel matrix. Explain why this can be beneficial.

assignments/assignment3.1380941519.txt.gz ยท Last modified: 2016/08/09 10:25 (external edit)

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