Exploring 10 601 Machine Learning Spring 2015 Lecture 18
Exploring 10 601 Machine Learning Spring 2015 Lecture 18 reveals several interesting facts.
- Topics: support vector
- Topics: kernel methods, margin, kernelizing a
- Topics: inference in graphical models, expectation maximization (EM)
- Topics: wrap-up of semi-supervised
- Topics: generalization error of Adaboost, margin, perceptron algorithm
In-Depth Information on 10 601 Machine Learning Spring 2015 Lecture 18
Topics: support vector Topics: semi-supervised Topics: Logistic regression and its relation to naive Bayes, gradient descent Lecture 18
Topics: high-level overview of
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