Instance Selection and Construction Seminar 2001

(2001. 5)

Participants: J.-S. Yang, J.-W. Lee, S.-B. Park, J.-H. Chang, K.-B. Hwang, J.-M. Oh, D.-Y.Cho, S.-Y. Shin Y.-H. Kim

Material: Instance Selection and Construction for Data Mining, Huan Liu and Hiroshi Motoda, 2001
Time and date: Mon, Wed 13:30-3:30
Location: 301-421

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

 


This page is maintained by Jeong-Ho Chang
Last update: May 14, 2001