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.

How to format and ship results

Results File Formats

The results on each dataset should be formatted in ASCII files according to the following table. If you are a Matlab user, you may find some of the sample code routines useful for formatting the data (CLOP users, the sample code is part of CLOP). You can view an example of each format from the filename column. You can optionally include your model in the submission if you are a CLOP user.

Filename Development Final entries Description File Format
[dataname]_train.resu Optional Compulsory Classifier outputs for training examples +/-1 indicating class prediction.
[dataname]_valid.resu Compulsory Compulsory Classifier outputs for validation examples
[dataname]_test.resu Optional Compulsory Classifier outputs for test examples
[dataname]_train.conf Optional+ Optional+ Classifier confidence for training examples Non-negative real numbers indicating the confidence in the classification (large values indicating higher confidence). They do not need to be probabilities, and can be simply absolute values of discriminant values. Optionally they can be normalized between 0 and 1 to be interpreted as abs(P(y=1|x)-P(y=-1|x)).
[dataname]_valid.conf Optional+ Optional+ Classifier confidence for validation examples
[dataname]_test.conf Optional+ Optional+ Classifier confidence for test examples
[dataname]_model.mat Optional# Optional# The trained CLOP model used to compute the submitted results A Matlab learning object saved with the command save_model([dataname '_model'], your_model, 1, 1).*
+ If no confidence file is supplied, equal confidence will be assumed for each classification. If confidences are not between 0 and 1, they will be divided by their maximum value.
* Setting the 2 last arguments to 1 forces overwriting models with the same name and saving only the hyperparameters of the model, not the parameters resulting from training. There is a limit on the size of the archive you can upload, so you will need to set the last argument to one.

Results Archive Format

Submitted files must be in either a .zip or .tar.gz archive format. You can download the example zip archive to help familiarise yourself with the archive structures and contents (the results were generated with the sample code). Submitted files must use exactly the same filenames as in the example archive. If you use tar.gz archives please do not include any leading directory names for the files. Use

zip results.zip *.resu *.conf *.mat
or
tar cvf results.tar *.resu *.conf *.mat; gzip results.tar
to create valid archives.

Synopsis of the competition rules