Event overview
Hosted by the Department of Computing, this seminar by Godfried Toussaint is entitled 'Proximity-Graph Instance-Based Learning, Support Vector Machines, and High Dimensionality: An Empirical Comparison'
Seminar by Godfried Toussaint
Faculty of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
http://nyuad.nyu.edu/academics/faculty/godfried-toussaint.html
Prof. Emeritus, McGill University
http://cgm.cs.mcgill.ca/~godfried/
Abstract:
Previous experiments with low dimensional data sets have shown that Gabriel graph methods for instance-based learning are among the best machine learning algorithms for pattern classification applications. However, as the dimensionality of the data grows large, all data points in the training set tend to become Gabriel neighbors of each other, bringing the efficacy of this method into question. Indeed, it has been conjectured that for high dimensional data, proximity graph methods that use sparser graphs, such as relative neighbor graphs (RNG) and minimum spanning trees (MST) would have to be employed in order to maintain their privileged status. Here the performance of proximity graph methods, in instance-based learning, that employ Gabriel graphs, relative neighborhood graphs, and minimum spanning trees, are compared experimentally on high-dimensional data sets. These methods are also compared empirically against the traditional k-NN rule and support vector machines (SVMs), the leading competitors of proximity graph methods.
References:
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Proximity Graphs:
http://cgm.cs.mcgill.ca/~godfried/research/proximity.html
PRIMAL:Proximity-graph Instance-based Machine Learning
http://www.eecs.tufts.edu/%7Egwg/primal/index.html
Nearest Neighbor Classification:
http://cgm.cs.mcgill.ca/~godfried/research/nearest.neighbor.html
G. T. Toussaint, "Geometric proximity graphs for improving nearest neighbor methods in instance-based learning and data mining," International Journal of Computational Geometry and Applications, Vol. 15, No. 2, April 2005, pp. 101-150.
http://www.worldscientific.com/doi/abs/10.1142/S0218195905001622
Dates & times
| Date | Time | Add to calendar |
|---|---|---|
| 12 Oct 2012 | 2:00pm - 3:00pm |
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