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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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Journal ArticleDOI
Real-time classification of evoked emotions using facial feature tracking and physiological responses
Jeremy N. Bailenson,Emmanuel D. Pontikakis,Iris B. Mauss,James J. Gross,Maria E. Jabon,Cendri A. Hutcherson,Clifford Nass,Oliver P. John +7 more
TL;DR: automated, real-time models built with machine learning algorithms which use videotapes of subjects' faces in conjunction with physiological measurements to predict rated emotion (trained coders' second-by-second assessments of sadness or amusement) are presented.
Proceedings Article
Constructing diverse classifier ensembles using artificial training examples
Prem Melville,Raymond J. Mooney +1 more
TL;DR: A new method for generating ensembles that directly constructs diverse hypotheses using additional artificially-constructed training examples is presented, which consistently achieves higher predictive accuracy than both the base classifier and bagging, and also obtains higher accuracy than boosting early in the learning curve when training data is limited.
Journal ArticleDOI
A 7 gene signature identifies the risk of developing cirrhosis in patients with chronic hepatitis C
Hongjin Huang,Mitchell L. Shiffman,Scott L. Friedman,Ramasubbu Venkatesh,Natalie Bzowej,Olivia T. Abar,Charles M. Rowland,Joseph J. Catanese,Diane U. Leong,John J. Sninsky,Thomas J. Layden,Teresa L. Wright,Thomas J. White,Ramsey Cheung +13 more
TL;DR: CRS is a better predictor than clinical factors in differentiating high‐risk versus low‐risk for cirrhosis in Caucasian CHC patients.
Proceedings ArticleDOI
Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces
TL;DR: An experiment is conducted to explore the potential of exploiting muscular sensing and processing technologies for muCIs, and results demonstrating accurate gesture classification with an off-the-shelf electromyography (EMG) device are presented.
Journal ArticleDOI
Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery
TL;DR: In this paper, the spectral information provided by the Landsat Thematic Mapper (TM) data set and the same classification scheme over Guangzhou City, China, was tested with two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms.
References
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Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Support-Vector Networks
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TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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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.