Topics that were discussed:
- What turnkey method provides the biggest gains, and
under what circumstances?
- Are simple techniques (e.g. averaging) preferable
to meta-learners (e.g., full learners that need to
be trained and fine-tuned)?
- Is it even meaningful to ask these questions without
- Can we explain why ensembles improve performance?
- Is it better to use repeated runs of one algorithm or
of many different algorithms with different levels of
- Should one use all available data in training
the individual generalizers (each starting with different
initial algorithm parameters)?
- Should one use spatial partitions of the data (e.g.,
mixtures of experts)?
- Should one use statistical partitions of the data (e.g.,
- Should one use partitions of the data based on previous
performance (e.g., boosting)?
- Should one distort the spaces (e.g. ECOCs, adding noise to
inputs of individual learners)?
- Should learners be trained/constructed differently
if they'll be used in a turnkey technique than if they'd
be stand alone generalizers?
- Are there advantages in actively trying to reduce the
correlation among algorithms in a pool?
- How can the experimental gains due to turnkey algorithms
be reconciled with the "No Free Lunch" (NFL) results?
(e.g., what are the assumptions that lead to improvements?)
If you have any questions or comments, please
email Kagan Tumer.
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