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Interactive Inductive Learning Based Classification System

By Ilze Birzniece

Abstract

Inductive learning system learns classification from training examples and uses induced rules for classifying new instances. As the classification tasks are getting more complicated, a classifier may meet difficulties in class prediction. To improve predictive accuracy of the inductive learning classifier, collaborative approach between a machine and human expert would be useful. The proposed interactive inductive system in uncertain conditions can ask for human advice and improve its performance with a rule derived from this interaction. Interactive inductive learning based classification system is proposed to assist in a study course comparative analysis

Topics: Inductive learning, human-computer interaction, study course comparison
Publisher: IADIS Press
OAI identifier: oai:ortus.rtu.lv:10449
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