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CORR
2000
Springer
120views Education» more  CORR 2000»
13 years 7 months ago
Scaling Up Inductive Logic Programming by Learning from Interpretations
When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming ...
Hendrik Blockeel, Luc De Raedt, Nico Jacobs, Bart ...
KDD
2009
ACM
205views Data Mining» more  KDD 2009»
14 years 2 months ago
From active towards InterActive learning: using consideration information to improve labeling correctness
Data mining techniques have become central to many applications. Most of those applications rely on so called supervised learning algorithms, which learn from given examples in th...
Abraham Bernstein, Jiwen Li
MLDM
2007
Springer
14 years 1 months ago
Transductive Learning from Relational Data
Transduction is an inference mechanism “from particular to particular”. Its application to classification tasks implies the use of both labeled (training) data and unlabeled (...
Michelangelo Ceci, Annalisa Appice, Nicola Barile,...
CSB
2005
IEEE
189views Bioinformatics» more  CSB 2005»
14 years 1 months ago
Learning Yeast Gene Functions from Heterogeneous Sources of Data Using Hybrid Weighted Bayesian Networks
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
Xutao Deng, Huimin Geng, Hesham H. Ali
CVPR
2010
IEEE
14 years 3 months ago
Safety in Numbers: Learning Categories from Few Examples with Multi Model Knowledge Transfer
Learning object categories from small samples is a challenging problem, where machine learning tools can in general provide very few guarantees. Exploiting prior knowledge may be ...
Tatiana Tommasi, Francesco Orabona, Barbara Caputo