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Mastering Java Machine Learning

You're reading from Mastering Java Machine Learning A Java developer's guide to implementing machine learning and big data architectures

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785880513

Length 556 pages

Edition 1st Edition

Languages

Java

Concepts

Big Data

Authors (2):

Uday Kamath

Krishna Choppella

View More author details

Table of Contents (13) Chapters

Preface

1. Machine Learning Review FREE CHAPTER

2. Practical Approach to Real-World Supervised Learning

3. Unsupervised Machine Learning Techniques

4. Semi-Supervised and Active Learning

5. Real-Time Stream Machine Learning

6. Probabilistic Graph Modeling

7. Deep Learning

8. Text Mining and Natural Language Processing

9. Big Data Machine Learning – The Final Frontier

A. Linear Algebra

B. Probability

Index

Basic stream processing and computational techniques

We will now describe some basic computations that can be performed on the stream of data. If we must run summary operations such as aggregations or histograms with limits on memory and speed, we can be sure that some kind of trade-off will be needed. Two well-known types of approximations in these situations are:

ϵ Approximation: The computation is close to the exact value within the fraction ϵ of error.
(ϵ, δ) Approximation: The computation is close to the exact value within 1 ± ϵ with probability within 1 – δ.

Stream computations

We will illustrate some basic computations and aggregations to highlight the difference between batch and stream-based calculations when we must compute basic operations with constraints on memory and yet consider the entire data:

Frequency count or point queries: The generic technique of Count-Min Sketch has been successfully applied to perform various summarizations...

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Authors (2)

Kamath

Kamath

See other products by Kamath

Krishna Choppella

Krishna Choppella

Krishna Choppella builds tools and client solutions in his role as a solutions architect for analytics at BAE Systems Applied Intelligence. He has been programming in Java for 20 years. His interests are data science, functional programming, and distributed computing.

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