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» Learning to learn with the informative vector machine
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KDD
1994
ACM
118views Data Mining» more  KDD 1994»
15 years 10 months ago
Discovering Informative Patterns and Data Cleaning
Wepresent a methodfor discovering informative patterns from data. With this method,large databases can be reducedto only a few representative data entries. Ourframeworkencompasses...
Isabelle Guyon, Nada Matic, Vladimir Vapnik
CORR
2006
Springer
92views Education» more  CORR 2006»
15 years 6 months ago
Event-based Information Extraction for the biomedical domain: the Caderige project
This paper gives an overview of the Caderige project. This project involves teams from different areas (biology, machine learning, natural language processing) in order to develop...
Érick Alphonse, Sophie Aubin, Philippe Bess...
ICML
2004
IEEE
16 years 7 months ago
Learning first-order rules from data with multiple parts: applications on mining chemical compound data
Inductive learning of first-order theory based on examples has serious bottleneck in the enormous hypothesis search space needed, making existing learning approaches perform poorl...
Cholwich Nattee, Sukree Sinthupinyo, Masayuki Numa...
ECML
2001
Springer
15 years 11 months ago
Comparing the Bayes and Typicalness Frameworks
When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these...
Thomas Melluish, Craig Saunders, Ilia Nouretdinov,...
ML
2008
ACM
15 years 6 months ago
A bias/variance decomposition for models using collective inference
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...
Jennifer Neville, David Jensen