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Instance Selection and Construction for Data Mining

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

08.12.2010

Herausgeber

Huan Liu + weitere

Verlag

Springer Us

Seitenzahl

416

Maße (L/B/H)

23,5/15,5/2,4 cm

Gewicht

669 g

Auflage

Softcover reprint of hardcover 1st ed. 2001

Sprache

Englisch

ISBN

978-1-4419-4861-8

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

08.12.2010

Herausgeber

Verlag

Springer Us

Seitenzahl

416

Maße (L/B/H)

23,5/15,5/2,4 cm

Gewicht

669 g

Auflage

Softcover reprint of hardcover 1st ed. 2001

Sprache

Englisch

ISBN

978-1-4419-4861-8

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Instance Selection and Construction for Data Mining
  • Produktbild: Instance Selection and Construction for Data Mining
  • Foreword; R.S. Michalski. Preface. Acknowledgments. Contributing Authors. Part I: Background and Foundation. 1. Data Reduction via Instance Selection; H. Liu, H. Motoda. 2. Sampling: Knowing Whole from its Part; B. Gu, et al. 3. A Unifying View on Instance Selection; T. Reinartz. Part II: Instance Selection Methods. 4. Competence Guided Instance Selection for Case-Based Reasoning; B. Smyth, E. McKenna. 5. Identifying Competence-Critical Instances for Instance-Based Learners; H. Brighton, C. Mellish. 6. Genetic-Algorithm-Based Instance and Feature Selection; H. Ishibuchi, et al. 7. The Landmark Model: An Instance Selection Method for Time Series Data; C.-S. Perng, et al. Part III: Use of Sampling Methods. 8. Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms; C. Domingo, et al. 9. Progressive Sampling; F. Provost, et al. 10. Sampling Strategy for Building Decision Trees from Very Large Databases Comprising Many Continuous Attributes; J.-H. Chauchat, R. Rakotomalala. 11. Incremental Classification Using Tree-Based Sampling for Large Data; H. Yoon, et al. Part IV: Unconventional Methods. 12. Instance Construction via Likelihood-Based Data Squashing; D. Madigan, et al. 13. Learning via Prototype Generation and Filtering; W. Lam, et al. 14. Instance Selection Based on Hypertuples; >H. Wang. 15. KBIS: Using Domain Knowledge to Guide Instance Selection; P. Wright, J. Hodges. Part V: Instance Selection in Model Combination. 16.Instance Sampling for Boosted and Standalone Nearest Neighbor Classifiers; D.B. Skalak. 17. Prototype Selection Using Boosted Nearest-Neighbors; R. Nock, M. Sebban. 18. DAGGER: Instance Selection for Combining Multiple Models Learnt from Disjoint Subsets; W. Davies, P. Edwards. Part VI: Applications of Instance Selection. 19. Using Genetic Algorithms for Training Data Selection in RBF Networks; C.R. Reeves, D.R. Bush. 20. An Active Learning Formulation for Instance Selection with Applications to Object Detection; K.-K. Sung, P. Niyogi. 21. Filtering Noisy Instances and Outliers; D. Gamberger, N. Lavrač. 22. Instance Selection Based on Support Vector Machine; S. Sugaya, et al. Index.