Date |
Title |
Speaker |
PPT |
May 2 |
Chapter 1:
Data Reduction via Instance Selection |
Y.-H. Kim |
PPT |
May 2 |
Chapter 2:
Sampling: Knowing Whole from Its Part |
S.-Y. Shin |
PPT |
May 7 |
Chapter 3:
A Unifying View on Instance Selection |
J. -S. Yang |
PPT |
May 7 |
Chapter 4:
Competed Guided Instance Selection for Case-Based Reasoning |
J.-W. Lee |
PPT |
May 9 |
Chapter 5:
Identifying Competence-Critical Instances for Instace-Based Learners |
K.-B. Hwang |
PPT |
May 9 |
Chapter 6:
Genetic-Algorithm-Based Instance and Feature Selection |
S. -Y. Shin |
PPT |
May 14 |
Chapter 7:
The Landmark Model: An Instance Selection Method for Time Series Data |
D.-Y. Cho |
PPT |
May 14 |
Chapter 8:
Adaptive Sampling Methods for Scaling up Knowledge Discovery |
J.-S. Yang |
PPT |
May 16 |
Chapter 9:
Progressive Sampling |
S.-Y. Shin |
ppt |
May 16 |
Chapter 10:
Sampling Strategy for Building Decision Trees from Very Large Databases |
S.-B. Park |
PPT |
May 21 |
Chapter 11:
Incremental Classification Using Tree-Based Sampling for Large Data |
D.-Y. Cho |
PPT |
May 21 |
Chapter 12:
Instance Construction via Likelihood-Based Data Squashing |
J.-S. Yang |
PPT |
May 23 |
Chapter 13:
Learning via Prototype Generation and Filtering |
J.-H. Chang |
ppt |
May 23 |
Chapter 14:
Instance Selection Based on Hypertuples
| S.-B. Park |
ppt |
May 28 |
Chapter 15:
KBIS: Using Domain Knowledge to Guide Instance Selection
| K.-B. Hwang |
ppt |
May 28 |
Chapter 16:
Instance Sampling for Boosted and Standalone Nearest Neighbor Classifiers
| J.-M. Oh |
ppt |
May 30 |
Chapter 17:
Prototype Selection Using Boosted Nearest-Neighbors
| Y.-H. Kim |
ppt |
May 30 |
Chapter 18:
DAGGER: Instance Selection for Combining Multiple-Models Learnt from Disjoint Subsets
| J.-H. Chang |
ppt |
Jun. 4 |
Chapter 19:
Using Genetic Algorithms for Training Data Selection in RBF Networks
| D.-Y. Cho |
ppt |
Jun. 4 |
Chapter 20:
An Active Learning Formulations for Instance Selection with Applications to Object Detection
| J.-H. Chang |
ppt |
Jun. 11 |
Chapter 21:
Filtering Noisy Instances and Outliers
| J.-W. Lee |
ppt |
Jun. 11 |
Chapter 22:
Instance Selection Based on Support Vector Machine
| J.-M. Oh |
ppt |
