This is a full list of my publications, ordered roughly according to my
own personal preference, which is mostly reverse-chronological. Some
unpublished working papers are at the bottom of the list.
Click here
for help on viewing and printing the files.
If you have any comments or questions, you can
mail me a note.
Working Papers by George John
Some of these papers are in submission,
others were never submitted or were submitted
unsuccessfully, and yet others were submitted successfully but
the final version became sufficiently different from the submitted
version that I thought it would be worthwhile to make both available.
For the most part, either I'm no longer thinking
about these problems/algorithms, or I have thought further and
the work is published elsewhere. Still, there are some ideas here
that might prove useful to someone, so I have made the papers available.
Click on a title to get the postscript file.
For each one you can get just the "(Abstract)" which
is a text-only web page giving title, author info, the abstract,
and citation info for the paper. Look
here for published papers.
If you have any comments or questions, you can
mail me a note.
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When the Best Move Isn't Optimal: Q-learning with Exploration
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(Abstract) By George John. May, 1995.
Submitted to
NIPS*95.
This is a longer version of
my AAAI student abstract
in 1994 with the same title.
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Geometry-Based Learning Algorithms
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(Abstract)
By George John. March, 1993. I think there are some reasonable ideas
in here (namely, focusing on geometric and topological properties of
learned representations in machine learning), but the paper is a little
confused. I'm interested in implementing the convex hull learning algorithm
described in this paper, or hearing about someone else who's already done
it.
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Robust Soft-Entropy Neural Network Trees
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(Abstract)
By George John.
Submitted to
Advances in Neural Information Processing Systems 7
(Similar to the "Robust Linear Discriminant Trees" paper
on my main page, but includes some info on using neural networks
instead of linear discriminants. Not well organized.)
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Robust Linear Discriminant Trees
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(Abstract)
By George John. (Extended Abstract, paper forthcoming)
Accepted to Artificial Intelligence & Statistics.
(Actually, the paper has come forth. See the paper of the same
title on my publications page.)