WSLD is a compact dataset for evaluation and benchmarking of weakly supervised learning algorithms designed for categorical sequential data.

The goal of such learning algorithm is to discover the relationship between recurring patterns in the data sequences and the associated explanatory variables (data labeling). Algorithm performance is evaluated by its ability to infer correct labels for new (unseen) test sequences after an initial learning stage. Feel free to beat the baseline results with your own approach!

Please send your new results, comments, and improvement ideas by email.

Dataset description (.pdf).
Data download (.mat and .txt formats).