Exploring 10 601 Machine Learning Spring 2015 Lecture 18
Exploring 10 601 Machine Learning Spring 2015 Lecture 18 reveals several interesting facts.
- Topics: semi-supervised
- Okay so let's uh look at support vector
- Topics: wrap-up of semi-supervised
- Topics: generalization error of Adaboost, margin, perceptron algorithm
- Submodular Functions, Optimization, and Applications to
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 18
Topics: support vector Topics: high-level overview of Lecture 18 Topics: kernel methods, margin, kernelizing a
Topics: bias-variance tradeoff, introduction to graphical models, conditional independence
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