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Empirical Inference Article SVMs — a practical consequence of learning theory Schölkopf, B. IEEE Intelligent Systems and their Applications, 13(4):18-21, July 1998
My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently.
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Empirical Inference Conference Paper From regularization operators to support vector kernels Smola, A., Schölkopf, B. In Advances in Neural Information Processing Systems, Advances in Neural Information Processing Systems 10, 343-349, (Editors: M Jordan and M Kearns and S Solla), MIT Press, Cambridge, MA, USA, 11th Annual Conference on Neural Information Processing (NIPS 1997), June 1998 PDF Web BibTeX
Empirical Inference Conference Paper Prior knowledge in support vector kernels Schölkopf, B., Simard, P., Smola, A., Vapnik, V. In Advances in Neural Information Processing Systems, Advances in Neural Information Processing Systems 10, 640-646 , (Editors: M Jordan and M Kearns and S Solla ), MIT Press, Cambridge, MA, USA, Eleventh Annual Conference on Neural Information Processing (NIPS 1997), June 1998 PDF Web BibTeX
Empirical Inference Article Learning view graphs for robot navigation Franz, M., Schölkopf, B., Mallot, H., Bülthoff, H. A. Autonomous Robots, 5(1):111-125, March 1998
We present a purely vision-based scheme for learning a topological representation of an open environment. The system represents selected places by local views of the surrounding scene, and finds traversable paths between them. The set of recorded views and their connections are combined into a graph model of the environment. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. In robot experiments, we demonstrate that complex visual exploration and navigation tasks can thus be performed without using metric information.
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