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ICML
2000
IEEE
16 years 2 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
106
Voted
ILP
2001
Springer
15 years 6 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
93
Voted
AO
2006
97views more  AO 2006»
15 years 1 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 8 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
102
Voted
EMNLP
2009
14 years 11 months ago
Learning Term-weighting Functions for Similarity Measures
Measuring the similarity between two texts is a fundamental problem in many NLP and IR applications. Among the existing approaches, the cosine measure of the term vectors represen...
Wen-tau Yih