Visual classification of medical data using MLP mapping.
In this work we discuss the design of a novel non-linear mapping method for visual classification based on multilayer perceptrons (MLP) and assigned class target values. In training the perceptron, one or more target output values for each class in a 2-dimensional space are used. In other words, class membership information is interpreted visually as closeness to target values in a 2D feature space. This mapping is obtained by training the multilayer perceptron (MLP) using class membership information, input data and judiciously chosen target values. Weights are estimated in such a way that each training feature of the corresponding class is forced to be mapped onto the corresponding 2-dimensional target value.
Cağatay Güler
E
E
Biomedical Engineering Institute, Boğaziçi University, Istanbul, Turkey.
Sankur
B
B
Kahya
Y P
YP
Raudys
S
S
D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification; D003196:Computer Graphics; D003626:Data Display; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008171:Lung Diseases / Q000175:diagnosis / Q000503:physiopathology; D008173:Lung Diseases, Obstructive / Q000175:diagnosis / Q000503:physiopathology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012119:Respiration; D012135:Respiratory Sounds / Q000175:diagnosis / Q000503:physiopathology