PASCAL Agnostic Learning
vs.
Prior Knowledge
IJCNN07

The challenge is now over. But it remains open for post-challenge submissions!


IMPORTANT: Entries made since February 1st 2007 might be using validation data, now available for training.

rbf lssvm

Submitted by reference

LS-SVM with radial basis function kernel, model selection by scaled conjugate gradient descent, minimising the PRESS statistic.

Performance Estimates:

ADA - BER = 0.188717 : AUC = 0.899603

GINA - BER = 0.057059 : AUC = 0.985336

HIVA - BER = 0.249459 : AUC = 0.809247

NOVA - BER = 0.063472 : AUC = 0.982794

SYLVA - BER = 0.007900 : AUC = 0.999044

Dataset Balanced Error Area Under Curve  
Train Valid Test Train Valid Test
ada 0.1576 0.2046 0.1887 0.927 0.8729 0.9 agnostic
gina 0 0.0442 0.0499 1 0.9955 0.9892 agnostic
hiva 0.0388 0.2819 0.2708 0.9983 0.7604 0.7771 agnostic
nova 0.0004 0.048 0.0619 1 0.9942 0.9852 agnostic
sylva 0.002 0.0049 0.0081 1 0.9991 0.9988 agnostic
Overall 0.0398 0.1167 0.1159 0.9851 0.9244 0.9301 agnostic