AQ-PM: A system for partial memory learning

Marcus A. Maloof and Ryszard S. Michalski

This paper describes AQ-PM, a system for partial memory learning, which determines and memorizes representative concept examples, and then uses them with new training examples to induce new concept descriptions. Our approach uses "extreme" examples that lie at the boundaries of current concept descriptions. We evaluated the system by applying it to synthetic and real-world learning problems. In the experiments, the partial memory learner notably reduced memory requirements for storing examples at the slight expense of predictive accuracy. The system also performed well when tracking concept drift.

Paper available in PostScript (gzipped) and PDF.

Maloof, M.A., & Michalski, R.S. (1999). AQ-PM: A system for partial memory learning. Proceedings of the Eighth Workshop on Intelligent Information Systems, 70–79. Warsaw, Poland: Polish Academy of Sciences.

@inproceedings{maloof.iis.99,
  author = "Maloof, M.A. and Michalski, R.S.",
  title = "{AQ-PM}: A system for partial memory learning",
  booktitle = "{Proceedings of the Eighth Workshop on Intelligent
    Information Systems}",
  pages = "70--79",
  year = 1999,
  publisher = "Polish Academy of Sciences",
  address = "Warsaw, Poland"
}