Pattern analysis by active learning method classifier

摘要: Active Learning Method ALM is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as macro-level brain imitation, has been inspired by some behavioral specifications and active learning ability. adaptive recursive which complex Multi Input, Output system can be represented combination several Single-Input, Single-Output systems. SISO systems associative layer algorithm capture partial spatial knowledge sample data space, enable granular resolution tuning mechanism through the process. The each sub-system its effectiveness whole would extracted Ink Drop Spread brief IDS operator consolidated using Fuzzy Rule Base FRB, acquire expert knowledge. In this paper we investigate conspicuous classifier different types classification problems. Also, new architecture actively analyze ill-balanced image patterns proposed. Different sets are used benchmark, including remote sensing problem, evaluate Classifier ALMC. With pattern generation ability tuning, ALMC distinguished from many conventional tools especially for structures analysis. work demonstrates that good noise robust classifier, adaptively adjusted structural evolution evaluation mechanism. These remarkable capabilities, along with straightforward process, make convenient tool use low dimensional recognition