Event Title
Location
Science Center Auditorium, University of Dayton
Start Date
22-4-2016 4:00 PM
Description
In our research program, we are developing machine learning algorithms to enable a mobile robot to build a compact representation of its environment. This requires the processing of each new input to terminate in constant time. Existing machine learning algorithms are either incapable of meeting this constraint or deliver problematic results. In this paper, we describe a new algorithm for real-time unsupervised clustering, Bounded Self-Organizing Clustering. It executes in constant time for each input, and it produces clusterings that are significantly better than those created by the Self-Organizing Map, its closest competitor, on sensor data acquired from a physically embodied mobile robot.
Included in
Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons, Other Computer Sciences Commons, Theory and Algorithms Commons
Real-time Unsupervised Clustering
Science Center Auditorium, University of Dayton
In our research program, we are developing machine learning algorithms to enable a mobile robot to build a compact representation of its environment. This requires the processing of each new input to terminate in constant time. Existing machine learning algorithms are either incapable of meeting this constraint or deliver problematic results. In this paper, we describe a new algorithm for real-time unsupervised clustering, Bounded Self-Organizing Clustering. It executes in constant time for each input, and it produces clusterings that are significantly better than those created by the Self-Organizing Map, its closest competitor, on sensor data acquired from a physically embodied mobile robot.
Comments
Copyright © 2016 by the author. This paper was presented at the 2016 Modern Artificial Intelligence and Cognitive Science Conference, held at the University of Dayton April 22-23, 2016. Permission documentation is on file.