FOSSIL: A Robust Relational Learner

Active relational rule learning in a constrained confidence rated boosting framework

In this dissertation, I investigate the potential of boosting within the framework of relational rule learning. Boosting is a particularly robust and powerful technique to enhance the prediction accuracy of systems that learn from examples. Although boosting has been extensively studied in the last years for propositional learning systems, only little attention has been paid to boosting in rela...

Multi-relational data mining in Microsoft SQL

Most real life data are relational by nature. Database mining integration is an essential goal to be achieved. Microsoft SQL Server (MSSQL) seems to provide an interesting and promising environment to develop aggregated multi-relational data mining algorithms by using nested tables and the plug-in algorithm approach. However, it is currently unclear how these nested tables can best be used by d...

What Kinds of Relational Features Are Useful for Statistical Learning?

A workmanlike, but nevertheless very effective combination of statistical and relational learning uses a statistical learner to construct models with features identified (quite often, separately) by a relational learner. This form of model-building has a long history in Inductive Logic Programming (ILP), with roots in the early 1990s with the LINUS system. Additional work has also been done in ...

Brief Note Fossil Vertebrate Taxonomic Diversity Correlated with Outcrop Area

Proper: A Toolbox for Learning from Relational Data with Propositional and Multi-Instance Learners

Databases predominantly employ the relational model for data storage. To use this data in a propositional learner, a propositionalization step has to take place. Similarly, the data has to be transformed to be amenable to a multi-instance learner. The Proper Toolbox contains an extended version of RELAGGS, the Multi-Instance Learning Kit MILK, and can also combine the multi-instance data with a...