1. Least Squares SVM Ensemble via Diversity(Relationship) Learning
Hua Qiang

2. Contents Introduction Notation Ensemble via Diversity Learning
Least Squares SVM
Model & Optimization
Experiment 2

3. LS-SVM Ensemble via Diversity Leaning - Introduction
Ensemble methods, which train multiple learners for a set of data.
The diversity of the component learners has been recognized as a key to a good ensemble.

4. LS-SVM Ensemble via Diversity Leaning - Notation
— the set of K learners
— the training set
— the corresponding labels
the linear function for Li :

5. LS-SVM Ensemble via Diversity Leaning
LS-SVM Ensemble via Diversity Leaning - Ensemble via Diversity Learning
Ref. :Yang Yu, Yu-Feng Li and Zhi-Hua Zhou, Diversity Regularized Machine, ijcai2011.

6. LS-SVM Ensemble via Diversity Leaning
LS-SVM Ensemble via Diversity Leaning - Ensemble via Diversity Learning
Ref. :Yang Yu, Yu-Feng Li and Zhi-Hua Zhou, Diversity Regularized Machine, ijcai2011. 6

7. LS-SVM Ensemble via Diversity Leaning
LS-SVM Ensemble via Diversity Leaning - Ensemble via Diversity Learning 7

8. LS-SVM Ensemble via Diversity Leaning
LS-SVM Ensemble via Diversity Leaning - Ensemble via Diversity Learning 8

9. LS-SVM Ensemble via Diversity Leaning - Least Squares SVM 9

10. LS-SVM Ensemble via Diversity Leaning - Model & Optimization
Optimizing w.r.t W and b when is fixed
Optimizing w.r.t when W and b are fixed 10

11. LS-SVM Ensemble via Diversity Leaning - Model & Optimization
Optimizing w.r.t W and b when is fixed 11

12. LS-SVM Ensemble via Diversity Leaning - Model & Optimization
Optimizing w.r.t when W and b are fixed 12

13. LS-SVM Ensemble via Diversity Leaning - Experiment
data
DRM（best）
SVM
SVM(lib)
Ours(mean)
austra
0.859
0.862
0.8259
*0.8634(0.02)
australian
0.860
0.8541
*0.8616
breastw
0.937
0.936
*0.9692
0.9625(0.02)
clean1
0.642
0.488
0.6080
*0.7835
diabetes
0.751
0.757
0.7391
*0.7664(0.01)
ethn
0.677
*0.9424
0.9376
german
0.720
0.713
0.7072
*0.7480
haberman
0.715
0.620
0.7368
*0.7401(0.05)
heart
0.804
0.810
*0.8333
0.8311(0.02)
house
0.9586
*0.9596
house-votes
0.903
0.9253
*0.9323(0.02)
ionosphere
0.674
0.663
0.8280
*0.8531
liver-disorders
*0.639
0.631
0.5814
0.5820
vehicle
0.779
0.9249
*0.9392
wdbc
0.969
0.7912
0.9349(0.01) 13

14. Thank You Very Much!