1 Data Reduction via Instance Selection. - 2 Sampling: Knowing Whole from Its Part. - 3 A Unifying View on Instance Selection. - 4 Competence Guided Instance Selection for Case-Based Reasoning. - 5 Identifying Competence-Critical Instances for Instance-Based Learners. - 6 Genetic-Algorithm-Based Instance and Feature Selection. - 7 The Landmark Model: An Instance Selection Method for Time Series Data. - 8 Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms. - 9 Progressive Sampling. - 10 Sampling Strategy for Building Decision Trees from Very Large Databases Comprising Many Continuous Attributes. - 11 Incremental Classification Using Tree-Based Sampling for Large Data. - 12 Instance Construction via Likelihood-Based Data Squashing. - 13 Learning via Prototype Generation and Filtering. - 14 Instance Selection Based on Hypertuples. - 15 KBIS: Using Domain Knowledge to Guide Instance Selection. - 16 Instance Sampling for Boosted and Standalone Nearest Neighbor Classifiers. - 17 Prototype Selection Using Boosted Nearest-Neighbors. - 18 DAGGER: Instance Selection for Combining Multiple Models Learnt from Disjoint Subsets. - 19 Using Genetic Algorithms for Training Data Selection in RBF Networks. - 20 An Active Learning Formulation for Instance Selection with Applications to Object Detection. - 21 Filtering Noisy Instances and Outliers. - 22 Instance Selection Based on Support Vector Machine. - Appendix: Meningoencepalitis Data Set. Language: English