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KDD
1994
ACM
140views Data Mining» more  KDD 1994»
15 years 11 months ago
A Comparison of Pruning Methods for Relational Concept Learning
Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are m...
Johannes Fürnkranz
189
Voted
CRV
2006
IEEE
171views Robotics» more  CRV 2006»
15 years 11 months ago
Underwater 3D Mapping: Experiences and Lessons learned
Abstract This paper provides details on the development of a tool to aid in 3D coral reef mapping designed to be operated by a single diver and later integrated into an autonomous ...
Andrew Hogue, Andrew German, James E. Zacher, Mich...
215
Voted
ESANN
2008
15 years 9 months ago
Generalized matrix learning vector quantizer for the analysis of spectral data
The analysis of spectral data constitutes new challenges for machine learning algorithms due to the functional nature of the data. Special attention is paid to the metric used in t...
Petra Schneider, Frank-Michael Schleif, Thomas Vil...
NIPS
2008
15 years 9 months ago
Learning Hybrid Models for Image Annotation with Partially Labeled Data
Extensive labeled data for image annotation systems, which learn to assign class labels to image regions, is difficult to obtain. We explore a hybrid model framework for utilizing...
Xuming He, Richard S. Zemel
211
Voted
ICAI
2004
15 years 9 months ago
Mixed-Initiative Ontology Learning
This paper presents a mixed-initiative assistant that supports a subject matter expert to extend the ontology of a learning agent, in order to express the subtle distinctions he ma...
Cristina Boicu, Gheorghe Tecuci