The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

The existence of fish species diversity in coastal ecosystems which include mangrove forests, seagrass beds and coral reefs is one the benchmarks determining health ecosystems. It certain that we must maintain, preserve care for so conservation efforts need to be carried out water areas. Many experts at Indonesian Fisheries Marine Research Development Agency often classify images manually, cour...

Gradient descent learning algorithms, namely Back Propagation (BP), can significantly increase the classification performance of Multi Layer Perceptrons adopting a local and adaptive learning rate management approach. In this paper, we present the comparison of the performance on hand-written characters classification of two BP algorithms, implementing fixed and adaptive learning rate. The resu...

cerebellar model articulation controller neural network is a computational model of cerebellum which acts as a lookup table. the advantages of cmac are fast learning convergence, and capability of mapping nonlinear functions due to its local generalization of weight updating, single structure and easy processing. in the training phase, the disadvantage of some cmac models is unstable phenomenon...

In this paper a high speed learning method using differential adaptive learning rate (DALRM) is proposed. Comparison of this method with other methods such as standard BP, Nguyen-Widrow weight Initialization and Optical BP shows that the network’s learning speed has highly increased. Learning often takes a long time to converge and it may fall into local minimas. One way of escaping from local ...

Multi-task learning (MTL) models have demonstrated impressive results in computer vision, natural language processing, and recommender systems. Even though many approaches been proposed, how well these balance different tasks on each parameter still remains unclear. In this paper, we propose to measure the task dominance degree of a by total updates parameter. Specifically, compute exponentiall...

in an effort to expand the disciplinary discussions on transfer in l2 writing and because most studies have focused on transfer as reuse and not as an adequate adaptation of writing knowledge in new contexts, the present study as the first of its kind aimed to explore the issue of adaptive transfer in an english for general academic purposes (egap) writing course. the study thus focused on type...

We present a novel per-dimension learning rate method for gradient descent called ADADELTA. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, var...