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Showing results 11 to 30 of 39
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Issue Date
Title
Author(s)
2011-12-01
Cataloguing/analogizing: A nonmonotonic view
Prade, H
;
Richard, G
2013
Classification Based on Homogeneous Logical Proportions.
Moraes, RMD
;
Machado, LS
;
Prade, H
;
Richard, G
;
Bramer, M
;
Petridis, M
2010-12-01
Classification by means of fuzzy analogy-related proportions - A preliminary report
Prade, H
;
Richard, G
;
Yao, B
2016-01-01
Completing preferences by means of analogical proportions
Pirlot, M
;
Prade, H
;
Richard, G
2016-11-01
Constructive Solving of Raven's IQ Tests with Analogical Proportions
Beltran, WC
;
Prade, H
;
Richard, G
2017-01-01
A discussion of analogical-proportion based inference
Prade, H
;
Richard, G
2012-05-01
Evaluation of analogical proportions through Kolmogorov complexity
Bayoudh, M
;
Prade, H
;
Richard, G
2015-01-01
Experimenting analogical reasoning in recommendation
Hug, N
;
Prade, H
;
Richard, G
2013-12-01
From analogical proportion to logical proportions
Prade, H
;
Richard, G
2014-01-01
From analogical proportion to logical proportions: A survey
Prade, H
;
Richard, G
2012-01-01
Homogeneous logical proportions: Their uniqueness and their role in similarity-based prediction
Prade, H
;
Richard, G
2011-12-01
A kolmogorov complexity view of analogy: From logical modeling to experimentations
Bayoudh, M
;
Prade, H
;
Richard, G
2011-12-21
Logical handling of analogical proportions in commonsense and transductive reasoning
Prade, H
;
Richard, G
2012-11-02
Logical proportions - Further investigations
Prade, H
;
Richard, G
2010-07-29
Logical proportions - Typology and roadmap
Prade, H
;
Richard, G
2010-08-12
Multiple-valued logic interpretations of analogical, reverse analogical, and paralogical proportions
Prade, H
;
Richard, G
2016-01-01
Not being at odds with a class: A new way of exploiting neighbors for classification
Bounhas, M
;
Prade, H
;
Richard, G
2018-12-01
Oddness-based classification: A new way of exploiting neighbors
Bounhas, M
;
Prade, H
;
Richard, G
2017-03-01
Oddness/evenness-based classifiers for Boolean or numerical data
Bounhas, M
;
Prade, H
;
Richard, G
2016-01-01
On different ways to be (dis)similar to elements in a set. Boolean analysis and graded extension
Prade, H
;
Richard, G
