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» Induction in Noisy Domains
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ICML
1998
IEEE
16 years 17 days ago
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
This paper introduces a new algorithm, Q2, foroptimizingthe expected output ofamultiinput noisy continuous function. Q2 is designed to need only a few experiments, it avoids stron...
Andrew W. Moore, Jeff G. Schneider, Justin A. Boya...
GIS
1992
ACM
15 years 3 months ago
Machine Induction of Geospatial Knowledge
Machine learning techniques such as tree induction have become accepted tools for developing generalisations of large data sets, typically for use with production rule systems in p...
Peter A. Whigham, Robert I. McKay, J. R. Davis
ACL
2010
14 years 9 months ago
Towards Open-Domain Semantic Role Labeling
Current Semantic Role Labeling technologies are based on inductive algorithms trained over large scale repositories of annotated examples. Frame-based systems currently make use o...
Danilo Croce, Cristina Giannone, Paolo Annesi, Rob...
GECCO
2003
Springer
123views Optimization» more  GECCO 2003»
15 years 5 months ago
Benefits of Implicit Redundant Genetic Algorithms for Structural Damage Detection in Noisy Environments
A robust structural damage detection method that can handle noisy frequency response function information is discussed. The inherent unstructured nature of damage detection problem...
Anne M. Raich, Tamás Liszkai
ICCBR
2005
Springer
15 years 5 months ago
CBR for Modeling Complex Systems
This paper describes how CBR can be used to compare, reuse, and adapt inductive models that represent complex systems. Complex systems are not well understood and therefore require...
Rosina Weber, Jason M. Proctor, Ilya Waldstein, An...