1-3hit |
Masanori HAMAMOTO Joarder KAMRUZZAMAN Yukio KUMAGAI Hiromitsu HIKITA
Fahlman and Lebiere's (FL) learning algorithm begins with a two-layer network and in course of training, can construct various network architectures. We applied FL algorithm to the same three-layer network architecture as a back propagation (BP) network and compared their generalization properties. Simulation results show that FL algorithm yields excellent saturation of hidden units which can not be achieved by BP algorithm and furthermore, has more desirable generalization ability than that of BP algorithm.
Kazuki ITO Masanori HAMAMOTO Joarder KAMRUZZAMAN Yukio KUMAGAI
A new neural network system for object recognition is proposed which is invariant to translation, scaling and rotation. The system consists of two parts. The first is a preprocessor which obtains projection from the input image plane such that the projection features are translation and scale invariant, and then adopts the Rapid Transform which makes the transformed outputs rotation invariant. The second part is a neural net classifier which receives the outputs of preprocessing part as the input signals. The most attractive feature of this system is that, by using only a simple shift invariant transformation (Rapid transformation) in conjunction with the projection of the input image plane, invariancy is achieved and the system is of reasonably small size. Experiments with six geometrical objects with different degrees of scaling and rotation shows that the proposed system performs excellent when the neural net classifier is trained by the Cascade-correlation learning algorithm proposed by Fahlman and Lebiere.
Masanori HAMAMOTO Joarder KAMRUZZAMAN Yukio KUMAGAI Hiromitsu HIKITA
We apply Fahlman and Lebiere's (FL) algorithm to network synthesis and incremental learning by making use of already-trained networks, each performing a specified task, to design a system that performs a global or extended task without destroying the information gained by the previously trained nets. Investigation shows that the synthesized or expanded FL networks have generalization ability superior to Back propagation (BP) networks in which the number of newly added hidden units must be pre-specified.