A promising trend in deep learning replaces traditional feedforward networks with implicit networks. Unlike networks, solve a fixed point equation to compute inferences. Solving for the varies complexity, depending on provided data and an error tolerance. Importantly, may be trained memory costs stark contrast whose requirements scale linearly depth. However, there is no free lunch --- backprop...

Feedforward nets with sigmoidal activation functions are often designed by minimizing a cost criterion. It has been pointed out before that this technique may be outperformed by the classical perceptron learning rule, at least on some problems. In this paper, we show that no such pathologies can arise if the error criterion is of a threshold LMS type, i.e., is zero for values “beyond” the desir...

Text classification gains lot of significance in the current scenario of processing and retrieval of text. Several algorithms are suggested for the text classification problem. This paper provides the solution by Back propagation network. The backpropagation network algorithm is adapted for the text classification. Before providing the algorithm the techniques used for feature identification is...

Rainfall is one of the important elements in process weather and climate. The high intensity rainfall every year can hamper mobility population distribution goods, especially port area. prediction needed to handle impacts caused by rainfall. data was obtained from website dataonline.bmkg.go.id with observations made Tanjung Perak Surabaya Maritime Meteorological Station. method uses an artifici...

Newton’s Method Backpropagation for Complex-Valued Holomorphic Neural Networks: Algebraic and Analytic Properties