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Scala for Machine Learning, Second Edition

You're reading from   Scala for Machine Learning, Second Edition Build systems for data processing, machine learning, and deep learning

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787122383
Length 740 pages
Edition 2nd Edition
Languages
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Author (1):
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Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
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Table of Contents (21) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Data Pipelines 3. Data Preprocessing 4. Unsupervised Learning 5. Dimension Reduction 6. Naïve Bayes Classifiers 7. Sequential Data Models 8. Monte Carlo Inference 9. Regression and Regularization 10. Multilayer Perceptron 11. Deep Learning 12. Kernel Models and SVM 13. Evolutionary Computing 14. Multiarmed Bandits 15. Reinforcement Learning 16. Parallelism in Scala and Akka 17. Apache Spark MLlib A. Basic Concepts B. References Index

Upper bound confidence

The UCB approach assumes that the expected reward of an action is linearly dependent on the d-dimension context.

Confidence interval

Intuitively, the confidence on the reward for a given arm increases as the arm is played. The variance of the reward is significantly high when the arm has been rarely played. The variance or confidence interval symbolizes the uncertainty on the reward of the arm. As the arm gets played, the confidence interval decreases.

The goal of the exploration is to play arms with a large confidence interval around the mean value of their reward so they can be a potential candidate for exploitation.

The following diagram illustrates the process of exploration [14:7]:

Confidence interval

Illustration of confidence interval for k arms during the exploitation-exploration cycle

The exploration phase favors the arm i being played to reduce its confidence interval. The exploration phase uses arm j because it has the highest mean reward with a very small confidence factor.

The...

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