Linguistic classification: T-norms, fuzzy distances and fuzzy distinguishabilities
| Autor: | Laura Franzoi, Andrea Sgarro |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Discrete mathematics
Class (set theory) Fuzzy classification business.industry Computer science Probabilistic logic T-norm Pattern recognition 0102 computer and information sciences 01 natural sciences Fuzzy logic 010201 computation theory & mathematics Truth value General Earth and Planetary Sciences Artificial intelligence business Cluster analysis General Environmental Science |
| Zdroj: | KES |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2017.08.163 |
| Popis: | Back in 1967 the linguist Ž. Muljacic used an additive distance between ill-defined linguistic features which is a forerunner of the fuzzy Hamming distance between strings of truth values in standard fuzzy logic. Here we show that if the logical frame is changed one obtains additive distances which are either sorely inadequate, as in the Łukasiewicz or probabilistic case, or coincide with the distance originally envisaged by Muljacic, as happens with a whole class of T-norms (abstract logical conjunctions) which includes the nilpotent minimum. All this strengthens the role of Muljacic distances in linguistic clustering and of Muljacic distinguishabilities (a notion subtly different from distances, but quite inalienable) in linguistic evolution. As a preliminary example we re-take and re-examine Muljacic original data. |
| Databáze: | OpenAIRE |
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