Overview
- Shows that a key functionality of several important Internet applications is actually the functionality of a classifier
- Describes various statistical and machine learning methods
- Includes classification methods with potential future use in applications
Part of the book series: Studies in Big Data (SBD, volume 69)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (7 chapters)
Keywords
About this book
This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender systems, malware detection, intrusion detection and sentiment analysis. It explains how such classification problems can be solved using various statistical and machine learning methods, including K nearest neighbours, Bayesian classifiers, the logit method, discriminant analysis, several kinds of artificial neural networks, support vector machines, classification trees and other kinds of rule-based methods, as well as random forests and other kinds of classifier ensembles. The book covers a wide range of available classification methods and their variants, not only those that have already been used in the considered kinds of applications, but also those that have the potential to be used in them in the future. The book is a valuable resource for post-graduate students and professionals alike.
Authors and Affiliations
About the authors
Martin Holeňa is senior researcher at the Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic.
Bibliographic Information
Book Title: Classification Methods for Internet Applications
Authors: Martin Holeňa, Petr Pulc, Martin Kopp
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-030-36962-0
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-36961-3Published: 30 January 2020
Softcover ISBN: 978-3-030-36964-4Published: 30 January 2021
eBook ISBN: 978-3-030-36962-0Published: 29 January 2020
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XII, 281
Number of Illustrations: 32 b/w illustrations, 29 illustrations in colour
Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Probability and Statistics in Computer Science, Pattern Recognition