Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learning
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TLDR
The notion of analogical dissimilarity is defined, which is a measure of how far four objects are from being in analogical proportion, and also learning algorithms, i.e. methods to find the triple of objects in a learning sample which has the least analogical Dissimilarity with a given object.Abstract:
This paper defines the notion of analogical dissimilarity be tween four objects, with a special focus on objects structured as sequences. Firstly, it studi es the case where the four objects have a null analogical dissimilarity, i.e. are in analogical proportion. Secondly, when one of these objects is unknown, it gives algorithms to compute it. Thirdly, it ta ckles the problem of defining analogical dissimilarity, which is a measure of how far four objects are from being in analogical proportion. In particular, when objects are sequences, it gives a definitio n and an algorithm based on an optimal alignment of the four sequences. It gives also learning algorithms, i.e. methods to find the triple of objects in a learning sample which has the least analogical dissimilarity with a given object. Two practical experiments are described: the first is a class ification problem on benchmarks of binary and nominal data, the second shows how the generation of sequences by solving analogical equations enables a handwritten character recognition system to rapidly be adapted to a new writer.read more
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Journal ArticleDOI
Machine learning
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Pattern Recognition and Machine Learning
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Book ChapterDOI
Handling Analogical Proportions in Classical Logic and Fuzzy Logics Settings
Laurent Miclet,Henri Prade +1 more
TL;DR: This work proposes a logical encoding of analogical proportions in a propositional setting, which is then extended to different fuzzy logics, and the fuzzy formalizations that are obtained parallel numerical models of analogICAL proportions.
Journal ArticleDOI
From Analogical Proportion to Logical Proportions
Henri Prade,Gilles Richard +1 more
TL;DR: It appears that only four proportions (including analogical proportion) are homogeneous in the sense that they use only one type of indicator (either similarity or dissimilarity) in their definition.
Journal ArticleDOI
Inducing semantic relations from conceptual spaces
Joaquín Derrac,Steven Schockaert +1 more
TL;DR: This paper first induces a conceptual space from the text documents, then relies on the key insight that the required semantic relations correspond to qualitative spatial relations in this conceptual space, and experimentally shows that these classifiers can outperform standard approaches, while being able to provide intuitive explanations of classification decisions.
References
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Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
A general method applicable to the search for similarities in the amino acid sequence of two proteins
TL;DR: A computer adaptable method for finding similarities in the amino acid sequences of two proteins has been developed and it is possible to determine whether significant homology exists between the proteins to trace their possible evolutionary development.