Machine Learning and Formal Concept Analysis
نویسنده
چکیده
منابع مشابه
Machine Learning methods and applications using Formal Concept Analysis
Machine learning (ML) deals with algorithms that automatically improve with experience where the experience for a ML algorithm is huge repositories of data. Machine learning methods produce a program that fits data to a model from lots of examples that specify the correct output for a given input. Formal Concept Analysis (FCA) is a successful model of learning from positive and negative example...
متن کاملA generalized concept-cognitive learning: A machine learning viewpoint
Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive computing (CC), conceptcognitive computing (CCC) and CCL is not clearly described. To this end, we explain the relationship of CC, CCC and CCL. Then, we propose ...
متن کاملNew Taxonomy of Classification Methods Based on Formal Concepts Analysis
Data mining is an essential step in knowledge extraction from data. Various approaches have been proposed in supervised classification of data, among them approaches based on Formal Concept Analysis. In this paper we present a new taxonomy of classification methods based on Formal Concept Analysis.
متن کاملExtracting Decision Trees from Interval Pattern Concept Lattices
Formal Concept Analysis (FCA) and concept lattices have shown their effectiveness for binary clustering and concept learning. Moreover, several links between FCA and unsupervised data mining tasks such as itemset mining and association rules extraction have been emphasized. Several works also studied FCA in a supervised framework, showing that popular machine learning tools such as decision tre...
متن کاملTowards Combining Machine Learning with Attribute Exploration for Ontology Refinement
We propose a new method for knowledge acquisition and ontology refinement for the Semantic Web utilizing Linked Data available through remote SPARQL endpoints. This method is based on combination of the attribute exploration algorithm from formal concept analysis and the active learning approach from machine learning.
متن کامل