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Scala for Machine Learning

You're reading from   Scala for Machine Learning Leverage Scala and Machine Learning to construct and study systems that can learn from data

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783558742
Length 624 pages
Edition 1st 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 (15) Chapters Close

Preface 1. Getting Started 2. Hello World! FREE CHAPTER 3. Data Preprocessing 4. Unsupervised Learning 5. Naïve Bayes Classifiers 6. Regression and Regularization 7. Sequential Data Models 8. Kernel Models and Support Vector Machines 9. Artificial Neural Networks 10. Genetic Algorithms 11. Reinforcement Learning 12. Scalable Frameworks A. Basic Concepts Index

Genetic algorithm components

Genetic algorithms have the following three components:

  • Genetic encoding (and decoding): This is the conversion of a solution candidate and its components into the binary format (an array of bits or a string of 0 and 1 characters)
  • Genetic operations: This is the application of a set of operators to extract the best (most genetically fit) candidates (chromosomes)
  • Genetic fitness function: This is the evaluation of the fittest candidate using an objective function

Encodings and the fitness function are problem dependent. Genetic operators are not.

Encodings

Let's consider the optimization problem in machine learning that consists of maximizing the log likelihood or minimizing the loss function. The goal is to compute the parameters or weights, w={wi}, that minimize or maximize a function f(w). In the case of a nonlinear model, variables may depend on other variables, which make the optimization problem particularly challenging.

Value encoding

The genetic algorithm...

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