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» Learning From Ambiguous Examples
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ICPR
2000
IEEE
15 years 10 months ago
General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of D
An important theoretical tool in machine learning is the bias/variance decomposition of the generalization error. It was introduced for the mean square error in [3]. The bias/vari...
Jakob Vogdrup Hansen, Tom Heskes
CP
2004
Springer
15 years 2 months ago
Leveraging the Learning Power of Examples in Automated Constraint Acquisition
Constraint programming is rapidly becoming the technology of choice for modeling and solving complex combinatorial problems. However, users of constraint programming technology nee...
Christian Bessière, Remi Coletta, Eugene C....
HIS
2007
14 years 11 months ago
Active Selection of Training Examples for Meta-Learning
Meta-Learning has been used to relate the performance of algorithms and the features of the problems being tackled. The knowledge in Meta-Learning is acquired from a set of meta-e...
Ricardo Bastos Cavalcante Prudêncio, Teresa ...
PADL
2009
Springer
15 years 10 months ago
Ad Hoc Data and the Token Ambiguity Problem
Abstract. PADS is a declarative language used to describe the syntax and semantic properties of ad hoc data sources such as financial transactions, server logs and scientific data ...
Qian Xi, Kathleen Fisher, David Walker, Kenny Qili...
ICML
2003
IEEE
15 years 10 months ago
Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labelin...
Wee Sun Lee, Bing Liu