AIC Seminar Series
Machine Learning via Advice Taking
|Jude Shavlik||University of Wisconsin-Madison||[Home Page]|
Notice: hosted by Sugato Basu
Date: Thursday, October 19th 2006 at 11:00am
Location: EJ228 (Directions)
Most research in machine learning focuses on a rather
narrow definition of training example.
Commonly, the learning algorithm is simply
given a list of "input-output" (I/O) pairs.
From these, the task of the machine learner is to induce a function that correctly replicates
all, or at least most, of these training examples
(and in addition accurately predicts the output
for inputs not seen during training). However,
a much richer sense of training example is possible,
one where the teacher provides broadly applicable
information, rather than just specific cases.
We present our recent work on creating
Knowledge-Based Support Vector Machines,
which are able to accept instruction beyond input-output pairs.
Since the learning algorithm is allowed to accept, refine, or discard this
instruction, we view the instruction as advice, as opposed to
commands, which computers must literally follow.
We also discuss how the advice-taking approach can be applied
to transfer learning; in this case, an algorithm automatically creates advice for a new task
by analyzing what was learned on a similar, prior task.
Jude Shavlik is a Professor of Computer Sciences and of
Biostatistics and Medical Informatics
at the University of Wisconsin - Madison, and is a Fellow of
the American Association for Artificial Intelligence.
He has been at Wisconsin since 1988, following the receipt
of his PhD from the University of Illinois for his
work on Explanation-Based Learning. His current research
interests include machine learning and computational biology,
with an emphasis on using rich sources of training information,
such as human-provided advice.
He served for three-years as editor-in-chief
of the AI Magazine and serves on the editorial board
of about a dozen journals. He chaired the 1998 International
Conference on Machine Learning, co-chaired the First
International Conference on Intelligent Systems for
Molecular Biology in 1993, co-chaired the First
International Conference on Knowledge Capture in 2001,
was conference chair of the 2003 IEEE Conference on Data Mining,
and will be co-chairing the 2007 International Conference on Inductive Logic Programming.
He was a founding member of both the board of the International Machine Learning Society
and the board of the International Society for Computational Biology.
He co-edited, with Tom Dietterich, "Readings in Machine Learning."
His research has been supported by DARPA, NSF, NIH, ONR, DOE, AT&T, IBM, and NYNEX.