Name
Abstract | ||
|---|---|---|
In recent works, analogy-based classifiers have been proved quite successful. They exhibit good accuracy rates when compared with standard classification methods. Nevertheless, a theoretical study of their predictive power has not been done so far. One of the main barriers has been the lack of functional definition: analogical learners have only algorithmic definitions. The aim of our paper is to complement the empirical studies with a theoretical perspective. Using a simplified framework, we first provide a concise functional definition of the output of an analogical learner. Two versions of the definition are considered, a strict and a relaxed one. As far as we know, this is the first definition of this kind for analogical learner. Then, taking inspiration from results in k-NN studies, we examine some analytic properties such as convergence and VC-dimension, which are among the basic markers in terms of machine learning expressiveness. We then look at what could be expected in terms of theoretical accuracy from such a learner, in a Boolean setting. We examine learning curves for artificial domains, providing experimental results that illustrate our formulas, and empirically validate our functional definition of analogical classifiers. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/978-1-61499-672-9-689 | Frontiers in Artificial Intelligence and Applications |
Field | DocType | Volume |
Convergence (routing),Predictive power,Computer science,Artificial intelligence,Analogy,Learning curve,Empirical research,Machine learning,Expressivity | Conference | 285 |
ISSN | Citations | PageRank |
0922-6389 | 2 | 0.37 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Hug | 1 | 10 | 2.55 |
| Henri Prade | 2 | 10549 | 1445.02 |
| Gilles Richard | 3 | 69 | 8.40 |
| Mathieu Serrurier | 4 | 267 | 26.94 |