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Mastering Machine Learning Algorithms

You're reading from Mastering Machine Learning Algorithms Expert techniques to implement popular machine learning algorithms and fine-tune your models

Product type Paperback

Published in May 2018

Publisher Packt

ISBN-13 9781788621113

Length 576 pages

Edition 1st Edition

Concepts

Machine Learning

Author (1):

Giuseppe Bonaccorso

View More author details

Table of Contents (17) Chapters

Preface

1. Machine Learning Model Fundamentals FREE CHAPTER

2. Introduction to Semi-Supervised Learning

3. Graph-Based Semi-Supervised Learning

4. Bayesian Networks and Hidden Markov Models

5. EM Algorithm and Applications

6. Hebbian Learning and Self-Organizing Maps

7. Clustering Algorithms

8. Ensemble Learning

9. Neural Networks for Machine Learning

10. Advanced Neural Models

11. Autoencoders

12. Generative Adversarial Networks

13. Deep Belief Networks

14. Introduction to Reinforcement Learning

15. Advanced Policy Estimation Algorithms

16. Other Books You May Enjoy

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Sanger's network

A Sanger's network is a neural network model for online Principal Component extraction proposed by T. D. Sanger in Optimal Unsupervised Learning in a Single-Layer Linear Feedforward Neural Network, Sanger T. D., Neural Networks, 1989/2. The author started with the standard version of Hebb's rule and modified it to be able to extract a variable number of principal components (v₁, v₂, ..., v_m) in descending order (λ₁ > λ₂ > ... > λ_m). The resulting approach, which is a natural extension of Oja's rule, has been called the Generalized Hebbian Rule (GHA) (or Learning). The structure of the network is represented in the following diagram:

The network is fed with samples extracted from an n-dimensional dataset:

The m output neurons are connected to the input through a weight matrix, W = {w_ij}, where the first index refers to the input components (pre-synaptic units) and the second one to the neuron. The output of the network can be easily computed with a scalar product;...

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