H. Osman et Mm. Fahmy, NEURAL CLASSIFIERS AND STATISTICAL PATTERN-RECOGNITION - APPLICATIONSFOR CURRENTLY ESTABLISHED LINKS, IEEE transactions on systems, man and cybernetics. Part B. Cybernetics, 27(3), 1997, pp. 488-497
Citations number
23
Categorie Soggetti
Controlo Theory & Cybernetics","Computer Science Cybernetics","Robotics & Automatic Control
Recent research has linked back-propagation (BP) and radial-basis-func
tion (RBF) network classifiers, trained by minimizing the standard mea
n-square error (MSE), to two main topics in statistical pattern recogn
ition (SPR), namely the Bayes decision theory and discriminant analysi
s. However, so far, the establishment of these links has resulted in o
nly a few practical applications for training, using, and evaluating t
hese classifiers, This paper aims at providing more of these applicati
ons, It first illustrates that while training a linear-output BP netwo
rk, the explicit utilization of the network discriminant capability le
ads to an improvement in its classification performance, Then, for lin
ear-output BP and RBF networks, the paper defines a new generalization
measure that provides information about the closeness of the network
classification performance to the optimal performance, The estimation
procedure of this measure is described and its use as an efficient cri
terion for terminating the learning algorithm and choosing the network
topology is explained, The paper finally proposes an upper bound on t
he number of hidden units needed by an RBF network classifier to achie
ve an arbitrary value of the minimized MSE, Experimental results are p
resented to validate all proposed applications.