er than mean squared error (MSE) function only. As an additional merit,

it is also revealed that rigorous Mercer kernel condition is not required in FKNN networks. When the

proposed

architecture of FKNN networks is constructed in a layer-by-layer way, i.e., the number of the

hidden

nodes of every hidden layer may be determined only in terms of the extracted principal com-

ponents

after the explicit execution of a KPCA, we can develop FKNN’s deep architecture such that its

deep

learning fr a mework (DLF) has strong theoretical guarantee. Our experimental results about image

classification

manifest that the proposed FKNN’s deep architecture and its DLF based learning indeed

enhance

the classification performance