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Machine Learning: End-to-End guide for Java developers

You're reading from   Machine Learning: End-to-End guide for Java developers Data Analysis, Machine Learning, and Neural Networks simplified

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Product type Course
Published in Oct 2017
Publisher Packt
ISBN-13 9781788622219
Length 1159 pages
Edition 1st Edition
Languages
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Authors (2):
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Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
Uday Kamath Uday Kamath
Author Profile Icon Uday Kamath
Uday Kamath
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Toc

Chapter 4. Semi-Supervised and Active Learning

In Chapter 2, Practical Approach to Real-World Supervised Learning and Chapter 3, Unsupervised Machine Learning Techniques, we discussed two major groups of machine learning techniques which apply to opposite situations when it comes to the availability of labeled data—one where all target values are known and the other where none are. In contrast, the techniques in this chapter address the situation when we must analyze and learn from data that is a mix of a small portion with labels and a large number of unlabeled instances.

In speech and image recognition, a vast quantity of data is available, and in various forms. However, the cost of labeling or classifying all that data is costly and therefore, in practice, the proportion of speech or images that are classified to those that are not classified is very small. Similarly, in web text or document classification, there are an enormous number of documents on the World Wide...

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