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Practical Machine Learning

You're reading from   Practical Machine Learning Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

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Product type Paperback
Published in Jan 2016
Publisher Packt
ISBN-13 9781784399689
Length 468 pages
Edition 1st Edition
Languages
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Author (1):
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Sunila Gollapudi Sunila Gollapudi
Author Profile Icon Sunila Gollapudi
Sunila Gollapudi
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Table of Contents (16) Chapters Close

Preface 1. Introduction to Machine learning FREE CHAPTER 2. Machine learning and Large-scale datasets 3. An Introduction to Hadoop's Architecture and Ecosystem 4. Machine Learning Tools, Libraries, and Frameworks 5. Decision Tree based learning 6. Instance and Kernel Methods Based Learning 7. Association Rules based learning 8. Clustering based learning 9. Bayesian learning 10. Regression based learning 11. Deep learning 12. Reinforcement learning 13. Ensemble learning 14. New generation data architectures for Machine learning Index

Kernel methods-based learning


We have just seen what instance-based learning methods are, and we have taken a deep dive into the Nearest Neighbor algorithm and covered specific implementation aspects. In this section, we will look into kernels and the kernel-based Machine learning algorithms.

A kernel, in simple terms, is a similarity function that is fed into a Machine learning algorithm. It takes two inputs and suggests how similar they are. For example, if we are dawned with a task of classifying images, the input data is a key-value pair (image, label). So, in terms of the flow, the image data is taken, features are computed, and a vector of features are fed into the Machine learning algorithm. But, in the case of similarity functions, we can define a kernel function that internally computes the similarity between images, and feed this into the learning algorithm along with the images and label data. The outcome of this is a classifier.

The standard regression or SVM or Perceptron frameworks...

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