Support vector machines (SVMs) try to divide two groups of data along a plane. An SVM finds the plane that is the farthest from both groups. If a plane comes much closer to group B, it will prefer a plane that is approximately an equal distance from both. SVMs have a number of nice properties. While other clustering or classification algorithms work well with defined clusters of data, SVMs may work fine with data that isn't in well-defined and delineated groupings. They are also not affected by the local minima. Algorithms such as K-Means or SOMs—which begin from a random starting point—can get caught in solutions that aren't bad for the area around the solution, but aren't the best for the entire space. This isn't a problem for SVMs.
Clojure Data Analysis Cookbook - Second Edition
By :
Clojure Data Analysis Cookbook - Second Edition
By:
Overview of this book
Table of Contents (19 chapters)
Clojure Data Analysis Cookbook Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Importing Data for Analysis
Cleaning and Validating Data
Managing Complexity with Concurrent Programming
Improving Performance with Parallel Programming
Distributed Data Processing with Cascalog
Working with Incanter Datasets
Statistical Data Analysis with Incanter
Working with Mathematica and R
Clustering, Classifying, and Working with Weka
Working with Unstructured and Textual Data
Graphing in Incanter
Creating Charts for the Web
Index
Customer Reviews