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ICPR
2000
IEEE
15 years 11 months ago
On Gaussian Radial Basis Function Approximations: Interpretation, Extensions, and Learning Strategies
In this paper we focus on an interpretation of Gaussian radial basis functions (GRBF) which motivates extensions and learning strategies. Specifically, we show that GRBF regressio...
Mário A. T. Figueiredo
ICML
2000
IEEE
15 years 10 months ago
Learning Subjective Functions with Large Margins
In manyoptimization and decision problems the objective function can be expressed as a linear combinationof competingcriteria, the weights of whichspecify the relative importanceo...
Claude-Nicolas Fiechter, Seth Rogers
ILP
2001
Springer
15 years 2 months ago
Learning Functions from Imperfect Positive Data
The Bayesian framework of learning from positive noise-free examples derived by Muggleton [12] is extended to learning functional hypotheses from positive examples containing norma...
Filip Zelezný
AO
2006
97views more  AO 2006»
14 years 10 months ago
An ontological model of device function: industrial deployment and lessons learned
Functionality is one of the key concepts of knowledge about artifacts. Functional knowledge shows a part of designer's intention (so-called design rationale), and thus its sha...
Yoshinobu Kitamura, Yusuke Koji, Riichiro Mizoguch...
JMLR
2010
121views more  JMLR 2010»
14 years 4 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor