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Titlebook: Data Mining Applications Using Artificial Adaptive Systems; William J. Tastle Book 2013 Springer Science+Business Media New York 2013 Data

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发表于 2025-3-21 19:35:24 | 显示全部楼层 |阅读模式
书目名称Data Mining Applications Using Artificial Adaptive Systems
编辑William J. Tastle
视频video
概述Advanced research in eight major areas with accompanying theory, proofs where appropriate, and illustrated by practical example.Readers will be able to immediately capture the meaning of the mathemati
图书封面Titlebook: Data Mining Applications Using Artificial Adaptive Systems;  William J. Tastle Book 2013 Springer Science+Business Media New York 2013 Data
描述This volume directly addresses the complexities involved in data mining and the development of new algorithms, built on an underlying theory consisting of linear and non-linear dynamics, data selection, filtering, and analysis, while including analytical projection and prediction. The results derived from the analysis are then further manipulated such that a visual representation is derived with an accompanying analysis. The book brings very current methods of analysis to the forefront of the discipline, provides researchers and practitioners the mathematical underpinning of the algorithms, and the non-specialist with a visual representation such that a valid understanding of the meaning of the adaptive system can be attained with careful attention to the visual representation. The book presents, as a collection of documents, sophisticated and meaningful methods that can be immediately understood and applied to various other disciplines of research. The content is composed of chapters addressing: An application of adaptive systems methodology in the field of post-radiation treatment involving brain volume differences in children; A new adaptive system for computer-aided diagnosis o
出版日期Book 2013
关键词Data Mining; Spatiotemporal mining; Unsupervised learning; adaptive systems; linear dynamics; multi-dimen
版次1
doihttps://doi.org/10.1007/978-1-4614-4223-3
isbn_softcover978-1-4939-4445-3
isbn_ebook978-1-4614-4223-3
copyrightSpringer Science+Business Media New York 2013
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发表于 2025-3-21 20:27:38 | 显示全部楼层
Meta Net: A New Meta-Classifier Family,An innovative taxonomy for the classification of classifiers is presented. This new family of meta-classifiers called Meta-Net, having its foundation in the theory of independent judges, is introduced, defined, described, and shown to possess very good performance when compared to other known meta-classifiers.
发表于 2025-3-22 04:22:48 | 显示全部楼层
William J. TastleAdvanced research in eight major areas with accompanying theory, proofs where appropriate, and illustrated by practical example.Readers will be able to immediately capture the meaning of the mathemati
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https://doi.org/10.1007/978-1-4614-4223-3Data Mining; Spatiotemporal mining; Unsupervised learning; adaptive systems; linear dynamics; multi-dimen
发表于 2025-3-22 16:13:35 | 显示全部楼层
978-1-4939-4445-3Springer Science+Business Media New York 2013
发表于 2025-3-22 18:47:18 | 显示全部楼层
Marco Antonio Pereira Querol,Laura Seppänenment for tumors in an effort to identify relationships that might yield knowledge in preventing brain volume loss in future treatments. Analysis of the pre- and post-treatment data is performed first using traditional statistics and then with the assistance of a new kind of artificial adaptive syste
发表于 2025-3-23 00:10:15 | 显示全部楼层
Marco Antonio Pereira Querol,Laura Seppänenish between benign and malignant lung nodules in Multi Detector Computed Tomography. A total of 90 nodules belonging to 88 patients are analyzed. A set of adjacent slices representing the lesion selected from the CT Image analysis by the experts are collected and stored in a database. Features extra
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