Machine Learning for Spatial and Temporal Data
Many emerging applications of machine learning require learning a
mapping y = F(x) where the xs and the
ys are complex objects such as time series, sequences,
2-dimensional maps, images, GIS layers, etc. Examples of such
applications include part-of-speech tagging, shallow parsing, various
forms of information extraction, landcover prediction in remote
sensing, protein secondary structure prediction, identifying
fraudulent transactions (telephone calls, fraudulent credit card
purchases, etc.), computer intrusion detection, identifying dangerous
situations in manufacturing time series, and classical problems such
as text-to-speech mapping and speech recognition.
Current off-the-shelf machine learning tools do not support these
kinds of tasks, so most applications projects have developed ad hoc
architectures and algorithms for solving them. Most of these ad hoc
systems have employed some form of divide-and-conquer in which each
(x, y) pair of complex objects is converted into a
series of overlapping windows where some region of the x object
is converted to a feature vector xi to
predict some individual yi value. These
(xi, yi) pairs are then
treated as if they were independent and identically distributed (iid)
training examples and fed to standard learning algorithms to learn a
window mapping yi =
f(xi). To process a new x object,
it is broken into windows, and the window mapping function f is
applied to map each window to a predicted yi. These
predicted values are then concatenated to produce a predicted y
This situation raises several challenges for machine learning research:
The purpose of this workshop is to bring together researchers from
several fields to discuss these challenges. Specifically, we will ask
the participants to identify the various existing approaches to
learning from spatial and temporal data, the state of the underlying
theory, the state of existing tools and tool kits, and the prospects
for developing new off-the-shelf tools.
- Are there better, systematic algorithms for spatio-temporal
learning than these divide-and-conquer methods? The
divide-and-conquer approach assumes the data are iid, which is not
true in most spatio-temporal learning situations where there may be
important spatial or temporal correlations in the data. How much does
this hurt in practice?
- Other disciplines have studied similar or identical
problems. How do they solve them? Can we import methods from
other disciplines to improve our divide-and-conquer systems?
- Many spatio-temporal learning problems have non-local loss
functions. Are there ways of handling these? In most
spatio-temporal applications, the measure of performance is not the
sum of the number of individual windows that are correctly classified
but rather some more global measure.
- Can off-the-shelf tools be designed and implemented that
incorporate best practices and automate the tedious aspects of
spatio-temporal learning? Can we incorporate such tools into
existing statistical and data mining packages?
Location: Thompson Chemistry, Room 202
8:30- 9:00 Introduction to research issues in spatio-temporal learning,
Tom Dietterich, Oregon State University
9:00- 10:00 15-minute talks on spatio-temporal applications
Foster Provost: Event monitoring for fraud
Attilio Giordana: Mining web/ftp logs
Simon Perkins: Spatio-spectral pixel classification in satellite images
Rene Quiniou: EKG monitoring and alarming in ICU's
10:00-10:15 COFFEE BREAK
10:15-11:15 15-minute talks on spatio-temporal applications
Alan Fern: Learning visual event definitions from video.
Mehmet Kayaal/Greg Cooper: Predicting survival outcomes from ICU time series
Rajesh Parekh & Ronny Kohavi: Temporal problems in e-commerce analysis
Cesare Furlanello: Finding unexploded bombs from WWII
11:15-12:00 Survey of HMM methods, Padhraic Smyth, UC Irvine
13:45-14:30 Spatial Methods in Image Analysis, Mike Turmon, JPL
14:30-15:15 Graph Transformer Networks and OCR, Leon Bottou, AT&T Research.
Also available in DejaVu Format.
15:30-15:45 COFFEE BREAK
15:45-16:30 Exponential Models for Sequential Data, John Lafferty, CMU
16:30-17:15 Rare Event Modeling and Validation Through Time: The case
of corporate credit analysis, Roger Stein, Moody's
17:15-17:45 Final Panel: Assembling a research agenda
- Thomas G. Dietterich, Department of Computer Science, Oregon
State University, Corvallis, OR 97331, firstname.lastname@example.org.
Provost, NYU, Information Systems Department Stern School of
Business, New York University 44 W. 4th St., New York, NY 10012-1126,
- Padhraic Smyth,
UC Irvine, Department of Information and Computer Science University
of California Irvine, CA 92697, email@example.com.
This material is based upon work supported by the National Science
Foundation under Grant No. 0083292. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of
the author(s) and do not necessarily reflect the views of the National