FREELessons: 20Length: 2.8 hours
- Overview
- Transcript
2.9 Support Vector Machines
Support Vector Machines (SVMs) attempt to find the best decision boundary between two output classes. They are considered the best off-the-shelf classifier because of their excellent performance. In this lesson we give an overview of the math behind SVMs and discuss two modifications, soft margins and kernels, that further improve their performance.
1.Introduction
1.1Introduction09:36
1.1
Introduction
09:36
1.2Machine Learning Basics09:00
1.2
Machine Learning Basics
09:00
2.Supervised Learning
2.1Supervised Learning Summary03:52
2.1
Supervised Learning Summary
03:52
2.2k-Nearest Neighbor08:37
2.2
k-Nearest Neighbor
08:37
2.3Decision Trees11:47
2.3
Decision Trees
11:47
2.4Perceptrons08:08
2.4
Perceptrons
08:08
2.5Linear Regression10:01
2.5
Linear Regression
10:01
2.6Naive Bayesian Classifiers06:57
2.6
Naive Bayesian Classifiers
06:57
2.7General Regression Neural Networks07:08
2.7
General Regression Neural Networks
07:08
2.8Feed-Forward Neural Networks14:19
2.8
Feed-Forward Neural Networks
14:19
2.9Support Vector Machines14:27
2.9
Support Vector Machines
14:27
2.10Random Forests07:56
2.10
Random Forests
07:56
3.Unsupervised Learning
3.1Unsupervised Learning Summary04:54
3.1
Unsupervised Learning Summary
04:54
3.2k-Means Clustering07:45
3.2
k-Means Clustering
07:45
3.3Hierarchical Clustering07:35
3.3
Hierarchical Clustering
07:35
3.4Self-Organizing Maps08:36
3.4
Self-Organizing Maps
08:36
3.5Apriori Association08:55
3.5
Apriori Association
08:55
4.Theory & Practice
4.1Theory12:10
4.1
Theory
12:10
4.2Practice05:50
4.2
Practice
05:50
5.Conclusion
5.1Wrap Up01:33
5.1
Wrap Up
01:33
