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KDD
1994
ACM
140views Data Mining» more  KDD 1994»
15 years 10 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
ESANN
2008
15 years 8 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 8 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
IQ
2007
15 years 8 months ago
An Alert Management Approach To Data Quality: Lessons Learned From The Visa Data Authority Program
: We introduce an end-to-end framework for data quality that integrates business strategy, data quality models, and supporting investigative and governance processes. We also descr...
Joseph Bugajski, Robert L. Grossman
ICAI
2004
15 years 8 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