ICML-2001 Workshop:
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 object.

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.


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


Other Notes

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.