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The Journal of Machine Learning Gossip (JMLG) supports
electronic distribution of high-quality articles describing
artificial studies in machine learning, artificial intelligence, and
related disciplines.
To contribute, please review our
Instructions for Authors. If you like the articles below,
check out these
other papers.
Volume 1
- Markov Indecision Processes: A Formal Model of
Decision-Making Under Extreme Confusion
- Harry Q. Bovik, Judy Q. Goldsmith, Andrew Q. Klapper,
Michael Q. Littman; 1(Apr):1-9, 2003.
Winner of the "Toughest submission to decide" Award:
ICML-2001
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We present a mathematical model of indecisive agents faced with a
sequence of difficult decisions, extending Adams' bistromathics to the
multistage case. This is almost the first work on modeling stochastic
processes for which the probabilities are fundamentally unknowable.
This paper describes a novel algorithm, complexity results, and a
model-free learning algorithm for Markov indecision processes. Two
applications are discussed based on real-world domains: presidential
elections and the stock market.
Data Set Selection
Doudou LaLoudouana and Mambobo Bonouliqui Tarare; 1(Apr):11-19, 2003.
Winner of the "Most original submission" Award:
NIPS-2002
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We introduce the community to a new construction principle whose
practical implications are very broad. Central to this research is the
idea of improving the presentation of algorithms in the literature and
making them more appealing. We define a new notion of capacity for
data sets and derive a methodology for selecting from them. Our
experiments demonstrate that even not-so-good algorithms can be shown
significantly better than competitors. We present some
experimental results, which are very promising.
On the Origin and Destiny of Inductive Machine Learning
Terran Lane; 1(Apr):21-27, 2003.
Winner of the "Only Submission with no Award" Award:
JMLG-2003
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Let not your prophets and your diviners, that be in the midst of you,
deceive you, neither hearken to your dreams which ye cause to be
dreamed.
Jeremiah, 29:8
Volume 2
- Facial Attraction: Symmetry Considered Harmful
- Nicol N. Schraudolph; 2(Apr):1-2, 2005.
[pdf]
An experiment in self-symmetrization yields surprising results about using
computers to make unattractive faces.
High Precision Document Classification in Over-Abundant Domains: A Case
Study of Web Pornography Search
Pieter van den Wankendon, William Johnson, and Randy Feller;
2(Apr):3-7, 2005.
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We present a small, but important, set of techniques that were found to boost
precision in web pornography search, as judged by volunteer annotators. Both
structured and statistical approaches yielded statistically significant
improvements in document classification. We detail the initial
corpus-development process, as well as empirical trials that demonstrate the
efficacy of our proposed techniques.
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