The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Based on our analysis and experiments using real-world datasets, we find that the greediness of forward feature selection algorithms doesn't severely corrupt the accuracy of the function approximation using the selected input features, but improves the efficiency significantly. Hence, we propose three greedier algorithms in order to further enhance the efficiency of the feature selection processing. We also propose to use these algorithms to develop an off-line Chinese and Japanese handwriting recognition system with automatically configured, local models.

Notes

Grant ID: NAGW-1175

Number of pages: 15

Text Reference

Kan Deng and Andrew Moore, "On Greediness of Feature Selection Algorithms," tech. report CMU-RI-TR-98-03, Robotics Institute, Carnegie Mellon University, February, 1998

BibTeX Reference

@techreport{Deng_1998_463,
   author = "Kan Deng and Andrew Moore",
   title = "On Greediness of Feature Selection Algorithms",
   booktitle = "",
   institution = "Robotics Institute",
   month = "February",
   year = "1998",
   number= "CMU-RI-TR-98-03",
   address= "Pittsburgh, PA",
}

The Robotics Institute

Menu