ISBN 10: 0792381963 / ISBN 13: 9780792381969
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This Book is in Good Condition. Clean Copy With Light Amount of Wear. 100% Guaranteed. Summary: Preface. Part I: Background and Foundation. 1. Less is More; Huan Liu, H. Motoda. 2. Feature Weighting for Lazy Learning Algorithms; D.W. Aha. 3. The Wrapper Approach; R. Kohavi, G.H. John. 4. Data-driven Constructive Induction: Methodology and Applications; E. Bloedorn, R.S. Michalski. Part II: Subset Selection. 5. Selecting Features by Vertical Compactness of Data; Ke Wang, S. Sundaresh. 6. Relevance Approach to Feature Subset Selection; Hui Wang, et al. 7. Novel Methods for Feature Subset Selection with Respect to Problem Knowledge; P. Pudil, J. Novovicov. 8. Feature Subset Selection Using a Genetic Algorithm; Jihoon Yang, V. Honavar. 9. A Relevancy Filter for Constructive Induction; N. Lavrac, et al. Part III: Feature Extraction. 10. Lexical Contextual Relations for the Unsupervised Discovery of Texts Features; P. Perrin, F. Petry. 11. Integrated Feature Extraction Using Adaptive Wavelets; Y. Mallet, et al. 12. Feature Extraction via Neural Networks; R. Setiono, Huan Liu. 13. Using Lattice-based Framework as a Tool for Feature Extraction; E. Mephu Nguifo, P. Njiwoua. 14. Constructive Function Approximation; P.E. Utgoff, D. Precup. Part IV: Feature Construction. 15. A Comparison of Constructing Different Types of New Feature for Decision Tree Learning; Zijian Zheng. 16. Constructive Induction: Covering Attribute Spectrum; Yuh-Jyh Hu. 17. Feature Construction Using Fragmentary Knowledge; S. Donoho, L. Rendell. 18. Constructive Induction on Continuous Spaces; J. Gama, P. Brazdil. Part V: Combined Approaches. 19. Evolutionary Feature Space Transformation; H. Vafaie, K. De Jong. 20. Feature Transformation by Function Decomposition; B. Zupan, et al. 21. Constructive Induction of Cartesian Product Attributes; M.J. Pazzani. Part VI: Applications of Feature Transformation. 22. Towards Automatic Fractal Feature Extraction for Image Recognition; M. Baldoni, et al. 23. Feature Transformation Strategies for a Robot Learning Problem; L.S. Lopes, L.M. Camarinha-Matos. 24. Interactive Genetic Algorithm Based Feature Selection and Its Application to Marketing Data Analysis; T. Terano, Y. Ishino. Index. Buchnummer des Verkäufers

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Inhaltsangabe: There is a broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data pre-processing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-the-art techniques that can boost the capabilities of many existing data mining tools.
The objective of this collection is to increase the awareness of the data mining community about research into feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of an endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches.
The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. The book can also serve as a reference work for those who are conducting research into feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.

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Huan Liu, Motoda, Hiroshi (eds.)
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ISBN 10: 0792381963 ISBN 13: 9780792381969
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Buchbeschreibung Kluwer Academic, 1998. Buchzustand: Fine. *Price HAS BEEN temporarily reduced until Tuesday, May 30 (holiday sale item)* xxiv, 410 pp., Hardcover, fine. Photos available upon request. Buchnummer des Verkäufers ZB1064652

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Huan Liu, Hiroshi Motoda
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Buchbeschreibung Kluwer Academic Publishers, United States, 1998. Hardback. Buchzustand: New. 1998 ed.. 244 x 163 mm. Language: English . Brand New Book. There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today s advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem. Buchnummer des Verkäufers AAU9780792381969

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Buchbeschreibung Kluwer Academic Publishers. hardback. Buchzustand: New. Buchnummer des Verkäufers 9780792381969

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Buchbeschreibung Kluwer Academic Publishers, 1998. HRD. Buchzustand: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Buchnummer des Verkäufers BB-9780792381969

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Liu, Huan, Huan, Liu
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ISBN 10: 0792381963 ISBN 13: 9780792381969
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Buchbeschreibung Springer, 1998. Buchzustand: New. Data pre-processing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from researchers in this field and offers the techniques that can boost the capabilities of many data mining tools. It is suitable for researchers and graduate students in machine learning and data mining. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 434 pages, biography. BIC Classification: UN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 235 x 155 x 25. Weight in Grams: 791. . 1998. 1998th Edition. Hardcover. . . . . Buchnummer des Verkäufers V9780792381969

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Huan Liu; Liu Huan
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Buchbeschreibung Springer, 1998. Buchzustand: New. book. Buchnummer des Verkäufers ria9780792381969_rkm

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Huan Liu, Hiroshi Motoda
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Buchbeschreibung Kluwer Academic Publishers. Hardback. Buchzustand: new. BRAND NEW, Feature Extraction, Construction and Selection: A Data Mining Perspective, Huan Liu, Hiroshi Motoda, There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem. Buchnummer des Verkäufers B9780792381969

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Huan Liu, Hiroshi Motoda
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ISBN 10: 0792381963 ISBN 13: 9780792381969
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Buchbeschreibung Kluwer Academic Publishers, United States, 1998. Hardback. Buchzustand: New. 1998 ed.. 244 x 163 mm. Language: English . Brand New Book. There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today s advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem. Buchnummer des Verkäufers AAU9780792381969

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Liu, Huan, Huan, Liu
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Buchbeschreibung Springer. Buchzustand: New. Data pre-processing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from researchers in this field and offers the techniques that can boost the capabilities of many data mining tools. It is suitable for researchers and graduate students in machine learning and data mining. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 434 pages, biography. BIC Classification: UN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 235 x 155 x 25. Weight in Grams: 791. . 1998. 1998th Edition. Hardcover. . . . . Buchnummer des Verkäufers V9780792381969

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Huan Liu; Liu Huan
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Buchbeschreibung Springer, 1998. Buchzustand: New. Buchnummer des Verkäufers EH9780792381969

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