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dc.contributor.authorGainanov, Damir
dc.description.abstractThis monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition. Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property – systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology. The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions.
dc.rightsopen access
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence::UYQV Computer vision
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TV Agriculture & farming
dc.subject.otherArtificial Intelligence
dc.subject.otherComputer Vision & Pattern Recognition
dc.subject.otherTechnology & Engineering
dc.titleGraphs for Pattern Recognition
dc.title.alternativeInfeasible Systems of Linear Inequalities
oapen.relation.isFundedByKnowledge Unlatched
oapen.collectionKnowledge Unlatched (KU)
oapen.imprintDe Gruyter

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open access
Except where otherwise noted, this item's license is described as open access