Support Vector Machines for Machine Learning
Last Updated on August 15, 2020
Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms.
They were extremely popular around the time they were developed in the 1990s and continue to be the go-to method for a high-performing algorithm with little tuning.
In this post you will discover the Support Vector Machine (SVM) machine learning algorithm. After reading this post you will know:
- How to disentangle the many names used to refer to support vector machines.
- The representation used by SVM when the model is actually stored on disk.
- How a learned SVM model representation can be used to make predictions for new data.
- How to learn an SVM model from training data.
- How to best prepare your data for the SVM algorithm.
- Where you might look to get more information on SVM.
SVM is an exciting algorithm and the concepts are relatively simple. This post was written for developers with little or no background in statistics and linear algebra.
As such we will stay high-level in this description and focus on the specific implementation concerns. The question around why specific equations are used or how they were derived are not
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