LM101-047: How Build a Support Vector Machine to Classify Patterns

LM101-047: How Build a Support Vector Machine to Classify Patterns (Rerun)

Listen now

Description

We explain how to estimate the parameters of such machines to classify a pattern vector as a member of one of two categories as well as identify special pattern vectors called “support vectors” which are important for characterizing the Support Vector Machine decision boundary. The relationship of Support Vector Machine parameter estimation and logistic regression parameter estimation is also discussed. For more information..check us out at: www.learningmachines101.com also check us out on twitter at: lm101talk

More Episodes

See all »

LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes

This 86th episode of Learning Machines 101 discusses the problem of assigning probabilities to a possibly infinite set of observed outcomes in a space-time continuum which corresponds to our physical world. The machine learning algorithm uses information about the frequency of environmental...

Published 07/20/21

Learning Machines 101

Published 07/20/21

LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges

This 85th episode of Learning Machines 101 discusses formal convergence guarantees for a broad class of machine learning algorithms designed to minimize smooth non-convex objective functions using batch learning methods. Simple mathematical formulas are presented based upon research from the late...

Published 05/21/21