02682nam a22003617a 4500 KnowledgeUnlatched 20210303104719.0 m o d cr u|||||||||| 210129p20162021xx o u00| u eng d BiblioBoard Graphs for Pattern Recognition Infeasible Systems of Linear Inequalities / Damir Gainanov. 9783110481068 https://doi.org/10.1515/9783110481068 https://library.biblioboard.com/ext/api/media/a80371dd-766f-4802-9ff9-c024f7263329/assets/thumbnail.jpg ScCtBLL ScCtBLL Gainanov, Damir author. De Gruyter, 1 online resource (1 p.) Access copy available to the general public. Unrestricted star This 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. Description based on print version record. KU Select 2019: STEM Backlist Books Technology & Engineering / Agriculture bisacsh Computers / Artificial Intelligence / Computer Vision & Pattern Recognition bisacsh Computers Electronic books. Is found in: Knowledge Unlatched https://openresearchlibrary.org/module/2774bc74-146a-484f-a7ba-ab1d6a09bbfb https://openresearchlibrary.org/content/a80371dd-766f-4802-9ff9-c024f7263329 View this content on Open Research Library. 0 32369 32369 0 0 lcc 0 0 DL DL OA 2021-03-03 0 2021-03-03 https://openresearchlibrary.org/content/a80371dd-766f-4802-9ff9-c024f7263329 2021-03-03 EBOOK