TY  - BOOK
AU  - Gainanov, Damir
AB  - 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.
DO  - https://doi.org/10.1515/9783110481068
ID  - OAPEN ID: https://library.oapen.org/handle/20.500.12657/46036
KW  - Computers
KW  - Artificial Intelligence
KW  - Computer Vision & Pattern Recognition
KW  - Technology & Engineering
KW  - Agriculture
L1  - https://library.oapen.org/bitstream/20.500.12657/46036/1/external_content.pdf
L1  - https://library.oapen.org/bitstream/20.500.12657/46036/1/external_content.pdf
LA  - English
LK  - https://directory.doabooks.org/handle/20.500.12854/30837
PB  - De Gruyter
PY  - 2016
SN  - 9783110481068
TI  - Graphs for Pattern Recognition : Infeasible Systems of Linear Inequalities
ER  - 
