ICML-2001 Workshop:
Machine Learning for Spatial and Temporal Data

Purpose

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 x_i to predict some individual y_i value. These (x_i, y_i) 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 y_i = f(x_i). 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 y_i. These predicted values are then concatenated to produce a predicted y object.

This situation raises several challenges for machine learning research:

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?

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.

Schedule

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
 
 12:00-13:30 Lunch

 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
             Workshop organizers

Organizers

Thomas G. Dietterich, Department of Computer Science, Oregon State University, Corvallis, OR 97331, tgd@cs.orst.edu.
Foster Provost, NYU, Information Systems Department Stern School of Business, New York University 44 W. 4th St., New York, NY 10012-1126, USA, fprovost@stern.nyu.edu.
Padhraic Smyth, UC Irvine, Department of Information and Computer Science University of California Irvine, CA 92697, smyth@ics.uci.edu.

Other Notes

This workshop was part of the Eighteenth International Conference on Machine Learning
The following paper provides background and motivation for this workshop: Dietterich, T. G. (2000). The Divide-and-Conquer Manifesto In Proceedings of the Eleventh International Conference on Algorithmic Learning Theory (pp. 13-26). New York: Springer-Verlag. Postscript Preprint. © Springer-Verlag.

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 Science Foundation.

ICML-2001 Workshop: Machine Learning for Spatial and Temporal Data

Purpose

Schedule

Organizers

Other Notes

ICML-2001 Workshop:
Machine Learning for Spatial and Temporal Data