You're reading from Hands-On Machine Learning with C++

Product typeBook

Published inMay 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781789955330

Edition1st Edition

Languages

C++

Tools

Caffe

Concepts

Machine Learning

Author (1)

Kirill Kolodiazhnyi

Exploring the applications of anomaly detection

There are two areas in data analysis that look for anomalies: outlier detection and novelty detection.

A new object or novelty is an object that differs in its properties from the objects in the training dataset. Unlike an outlier, the new object is not in the dataset itself, but it can appear at any point after a system has started working. Its task is to detect when it appears. For example, if we were to analyze existing temperature measurements and identify abnormally high or low values, then we would be detecting outliers. On the other hand, if we were to create an algorithm that, for every new measurement, evaluates the temperature's similarity to past values and identifies significantly unusual ones, then we are detecting novelties.

The reasons for outliers appearing include data errors, the presence of noise, misclassified...

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Hands-On Machine Learning with C++

Published in: May 2020Publisher: PacktISBN-13: 9781789955330

Author (1)

Kirill Kolodiazhnyi

Kirill Kolodiazhnyi is a seasoned software engineer with expertise in custom software development. He has several years of experience building machine learning models and data products using C++. He holds a bachelor degree in Computer Science from the Kharkiv National University of Radio-Electronics. He currently works in Kharkiv, Ukraine where he lives with his wife and daughter.
Read more about Kirill Kolodiazhnyi

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