Difference between revisions of "10-601B Perceptrons and Large Margin"
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* How to implement the voted perceptron. | * How to implement the voted perceptron. | ||
* The definition of a mistake bound, and a margin. | * The definition of a mistake bound, and a margin. | ||
| + | |||
| + | * The definitions of, and intuitions behind, these concepts: | ||
| + | ** The ''margin'' of a classifier relative to a dataset. | ||
| + | ** What a ''constrained optimization problem'' is. | ||
| + | ** The ''primal form'' of the SVM optimization problem. | ||
| + | ** The ''dual form'' of the SVM optimization problem. | ||
| + | ** What a ''support vector'' is. | ||
| + | ** What a ''kernel function'' is. | ||
| + | ** What ''slack variables'' are and why and when they are used in SVMs. | ||
| + | * How to explain the different parts (constraints, optimization criteria) of the primal and dual forms for the SVM. | ||
| + | * How the perceptron and SVM are similar and different. | ||
Revision as of 10:15, 12 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- My notes on the voted perceptron. (You can skip sections 3-4 on ranking and the structured perceptron).
- Optional reading: Freund, Yoav, and Robert E. Schapire. "Large margin classification using the perceptron algorithm." Machine learning 37.3 (1999): 277-296.
- Optional background on linear algebra: Zico Kolter's linear algebra review lectures
What You Should Know Afterward
- The difference between an on-line and batch algorithm.
- How to implement the voted perceptron.
- The definition of a mistake bound, and a margin.
- The definitions of, and intuitions behind, these concepts:
- The margin of a classifier relative to a dataset.
- What a constrained optimization problem is.
- The primal form of the SVM optimization problem.
- The dual form of the SVM optimization problem.
- What a support vector is.
- What a kernel function is.
- What slack variables are and why and when they are used in SVMs.
- How to explain the different parts (constraints, optimization criteria) of the primal and dual forms for the SVM.
- How the perceptron and SVM are similar and different.
