Refining Concepts by Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242944" target="_blank" >RIV/61989100:27240/19:10242944 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/47813059:19240/19:A0000487

Výsledek na webu

<a href="https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3242/2663" target="_blank" >https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3242/2663</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13053/CyS-23-3-3242" target="_blank" >10.13053/CyS-23-3-3242</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Refining Concepts by Machine Learning

Popis výsledku v původním jazyce

In this paper we deal with machine learning methods and algorithms applied in learning simple concepts by their refining or explication. The method of refining a simple concept of an object O consists in discovering a molecular concept that defines the same or a very similar object to the object O. Typically, such a molecular concept is a professional definition of the object, for instance a biological definition according to taxonomy, or legal definition of roles, acts, etc. Our background theory is Transparent Intensional Logic (TIL). In TIL concepts are explicated as abstract procedures encoded by natural language terms. These procedures are defined as six kinds of TIL constructions. First, we briefly introduce the method of learning with a supervisor that is applied in our case. Then we describe the algorithm &apos;Framework&apos; together with heuristic methods applied by it. The heuristics is based on a plausible supply of positive and negative (near-miss) examples by which learner&apos;s hypotheses are refined and adjusted. Given a positive example, the learner refines the hypothesis learnt so far, while a near-miss example triggers specialization. Our heuristic methods deal with the way refinement is applied, which includes also its special cases generalization and specialization.

Název v anglickém jazyce

Refining Concepts by Machine Learning

Popis výsledku anglicky

In this paper we deal with machine learning methods and algorithms applied in learning simple concepts by their refining or explication. The method of refining a simple concept of an object O consists in discovering a molecular concept that defines the same or a very similar object to the object O. Typically, such a molecular concept is a professional definition of the object, for instance a biological definition according to taxonomy, or legal definition of roles, acts, etc. Our background theory is Transparent Intensional Logic (TIL). In TIL concepts are explicated as abstract procedures encoded by natural language terms. These procedures are defined as six kinds of TIL constructions. First, we briefly introduce the method of learning with a supervisor that is applied in our case. Then we describe the algorithm &apos;Framework&apos; together with heuristic methods applied by it. The heuristics is based on a plausible supply of positive and negative (near-miss) examples by which learner&apos;s hypotheses are refined and adjusted. Given a positive example, the learner refines the hypothesis learnt so far, while a near-miss example triggers specialization. Our heuristic methods deal with the way refinement is applied, which includes also its special cases generalization and specialization.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA18-23891S" target="_blank" >GA18-23891S: Hyperintensionální usuzování nad texty přirozeného jazyka</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computación y Sistemas

ISSN

1405-5546

e-ISSN

—

Svazek periodika

23

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

MX - Spojené státy mexické

Počet stran výsledku

16

Strana od-do

943-958

Kód UT WoS článku

000489136900031

EID výsledku v databázi Scopus

2-s2.0-85076629969