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Data Mining: Practical Machine Learning Tools and Techniques
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TLDR
This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.Abstract:
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualizationread more
Citations
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Proceedings ArticleDOI
Which verifiers work?: A benchmark evaluation of touch-based authentication algorithms
TL;DR: Using a series of statistical tests, this paper rigorously compares the performance of ten state-of-the-art touch-based authentication classification algorithms under a common experimental protocol, and presents the associated benchmark dataset for the community to use.
Journal ArticleDOI
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade
TL;DR: This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade.
Proceedings ArticleDOI
Autonomous transfer for reinforcement learning
TL;DR: This paper introduces Modeling Approximate State Transitions by Exploiting Regression (MASTER), a method for automatically learning a mapping from one task to another through an agent's experience and demonstrates that such learned relationships can significantly improve the speed of a reinforcement learning algorithm in a series of Mountain Car tasks.
Journal ArticleDOI
Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework
TL;DR: The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework and indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.
Book ChapterDOI
Detection and analysis of off-task gaming behavior in intelligent tutoring systems
TL;DR: Machine-learned gaming-detection models were developed to investigate underlying factors related to gaming, and an analysis of gaming within the Assistments system was conducted to compare some of the findings of prior studies.
References
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Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
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A Coefficient of agreement for nominal Scales
TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.