Browsing by Author "CHIANG, CC"

Now showing 1 - 3 of 3

FEATURE-PRESERVING THINNING ALGORITHM FOR OPTICAL CHARACTER-RECOGNITION
(1995-07-01)
FU, HC
;
CHENG, TS
;
CHIANG, CC
;
ROAN, SM
;
交大名義發表
We propose an improved thinning algorithm. Basically, the algorithm uses a 3 x 3 window to accommodate an eight-neighbor skeleton in each thinning iteration. This algorithm overcomes many thinning problems, such as Y-shaped distortions, spiky skeletons and skeleton shortening, and thus preserves precise features of digital character patterns. By using this thinning algorithm, better structural features of a character pattern can be provided to an optical character recognition system, such that accurate recognition results can be achieved.
THE CLASSIFICATION CAPABILITY OF A DYNAMIC THRESHOLD NEURAL-NETWORK
(1994-04-01)
CHIANG, CC
;
FU, HC
;
資訊工程學系
;
Department of Computer Science
This paper proposes a new type of neural network called the Dynamic Threshold Neural Network (DTNN). Through theoretical analysis, we prove that the classification capability of a DTNN can be twice as effective as a conventional sigmoidal multilayer neural network in classification capability. In other words, to successfully learn an arbitrarily given training set, a DTNN may need as little as half the number of free parameters required by a sigmoidal multilayer neural network.
USING MULTITHRESHOLD QUADRATIC SIGMOIDAL NEURONS TO IMPROVE CLASSIFICATION CAPABILITY OF MULTILAYER PERCEPTRONS
(1994-05-01)
CHIANG, CC
;
FU, HC
;
資訊工程學系
;
Department of Computer Science
This letter proposes a new type of neurons called multithreshold quadratic sigmoidal neurons to improve the classification capability of muitilayer neural networks. In cooperation with single-threshold quadratic sigmoidal neurons, the multithreshold quadratic sigmoidal neurons can be used to improve the classification capability of multilayer neural networks by a factor of four compared to committee machines and by a factor of two compared to the conventional sigmoidal multilayer perceptrons.