Journal ArticleDOI
Data mining: practical machine learning tools and techniques with Java implementations
Ian H. Witten,Eibe Frank +1 more
- Vol. 31, Iss: 1, pp 76-77
TLDR
This presentation discusses the design and implementation of machine learning algorithms in Java, as well as some of the techniques used to develop and implement these algorithms.Abstract:
1. What's It All About? 2. Input: Concepts, Instances, Attributes 3. Output: Knowledge Representation 4. Algorithms: The Basic Methods 5. Credibility: Evaluating What's Been Learned 6. Implementations: Real Machine Learning Schemes 7. Moving On: Engineering The Input And Output 8. Nuts And Bolts: Machine Learning Algorithms In Java 9. Looking Forwardread more
Citations
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Journal ArticleDOI
The WEKA data mining software: an update
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Posted Content
Principles of data mining
TL;DR: This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions.
Book ChapterDOI
Activity recognition from user-annotated acceleration data
Ling Bao,Stephen S. Intille +1 more
TL;DR: This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves, and suggests that multiple accelerometers aid in recognition.
Journal ArticleDOI
From frequency to meaning: vector space models of semantics
Peter D. Turney,Patrick Pantel +1 more
TL;DR: The goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs, and to provide pointers into the literature for those who are less familiar with the field.
Journal Article
An extensive empirical study of feature selection metrics for text classification
TL;DR: An empirical comparison of twelve feature selection methods evaluated on a benchmark of 229 text classification problem instances, revealing that a new feature selection metric, called 'Bi-Normal Separation' (BNS), outperformed the others by a substantial margin in most situations and was the top single choice for all goals except precision.
References
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Formal definition of the concept "infos"
TL;DR: The concept INFOS is very important for understanding the information phenomena and it is basic for the General Information Theory.