Machine Learning with MATLAB: Getting Started with Classification
Classification is used to assign items to a discrete group or class based on a specific set of features. Classification algorithms are a core component of statistical learning / machine learning.
In this webinar we introduce the classification capabilities included in Statistics and Machine Learning Toolbox.
Representative examples include:
- Identifying vehicle type based on an acoustic signal or an image
- Sorting manufactured goods using images (optical quality control)
- Assigning a credit rating using information included in a financial statement
- Predicting a tumor type based on a DNA profile
- Filtering SPAM using the frequency of different words in an email
Applied examples using real world data sets are used to describe how you can:
- Choose between classification algorithms (bagged decision trees, naïve Bayes classifiers, discriminant analysis, and logistic regression)
- Train your classifier
- Evaluate the accuracy of a classifier (confusion matrices, ROC curves, classification error)
- Simplify your classification model
View the MATLAB code and data sets here.
About the Presenter: Richard Willey is a product marketing manager at MathWorks where he focuses on MATLAB and add-on products for data analysis, Statistics, and Curve Fitting. Prior to joining MathWorks in 2007, Richard worked at Wind River Systems and Symantec. Richard has dual master’s in Engineering and Management from the Massachusetts Institute of Technology and a master’s degree in Economics from Indiana University.
Recorded: 22 Sep 2011
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