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Mastering Predictive Analytics with R, Second Edition

You're reading from   Mastering Predictive Analytics with R, Second Edition Machine learning techniques for advanced models

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121393
Length 448 pages
Edition 2nd Edition
Languages
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Authors (2):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
Rui Miguel Forte Rui Miguel Forte
Author Profile Icon Rui Miguel Forte
Rui Miguel Forte
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Table of Contents (16) Chapters Close

Preface 1. Gearing Up for Predictive Modeling FREE CHAPTER 2. Tidying Data and Measuring Performance 3. Linear Regression 4. Generalized Linear Models 5. Neural Networks 6. Support Vector Machines 7. Tree-Based Methods 8. Dimensionality Reduction 9. Ensemble Methods 10. Probabilistic Graphical Models 11. Topic Modeling 12. Recommendation Systems 13. Scaling Up 14. Deep Learning Index

Kernels and support vector machines


So far, we've introduced the notion of maximum margin classification under linearly separable conditions and its extension to the support vector classifier, which still uses a hyperplane as the separating boundary but handles datasets that are not linearly separable by specifying a budget for tolerating errors. The observations that are on or within the margin, or are misclassified by the support vector classifier, are support vectors. The critical role that these play in the positioning of the decision boundary was also seen in an alternative model representation of the support vector classifier that uses inner products.

What is common in the situations that we have seen so far in this chapter is that our model is always linear in terms of the input features. We've seen that the ability to create models that implement nonlinear boundaries between the classes to be separated is far more flexible in terms of the different kinds of underlying target functions...

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