Gradient Omissive Descentis A Minimization Algorithm

Gradient Omissive Descentis A Minimization Algorithm
Gustavo A. Lado and Enrique C. Segura Universidad de Buenos Aires Cyprus
ABSTRACT
This article presents a promising new gradient-based backpropagation algorithm for multi-layer feedforward networks. The method requires no manual selection of global hyperparameters and is capable of dynamic local adaptations using only first-order information at a low computational cost. Its semi-stochastic nature makes it fit for mini-batch training and robust to different architecture choices and data distributions. Experimental evidence shows that the proposed algorithm improves training in terms of both convergence rate and speed as compared with other well known techniques.
KEYWORDS Neural Networks, Backpropagation, Cost Function Minimization Original Source URL: http://aircconline.com/ijscai/V8N1/8119ijscai03.pdf http://airccse.org/journal/ijscai/current2019.html

Comments

Post a Comment

Popular posts from this blog

Cancer Prognosis Prediction Using Balanced Stratified Sampling

Cancer Prognosis Prediction Using Balanced Stratified Sampling J.S.Saleema1, N.Bhagawathi2, S.Monica2, P.Deepa Shenoy2, K.R.Venugopal2 and L.M.Patnaik3, 1Christ University, India 2University Visvesvaraya College of Engineering, India 3Indian Institute of Science, India ABSTRACT High accuracy in cancer prediction is important to improve the quality of the treatment and to improve the rate of survivability of patients. As the data volume is increasing rapidly in the healthcare research, the analytical challenge exists in double. The use of effective sampling technique in classification algorithms always yields good prediction accuracy. The SEER public use cancer database provides various prominent class labels for prognosis prediction. The main objective of this paper is to find the effect of sampling techniques in classifying the prognosis variable and propose an ideal sampling method based on the outcome of the experimentation. In the first phase of this work the traditional rando...
Read more

Design and Implementation of Smart Cooking Based on Amazon Echo

Design and Implementation of Smart Cooking Based on Amazon Echo Lin Xiaoguang1,2,3, Yang Yong3 and Zhang Ju1,3 1University of Chinese Academy of Sciences, China 2Chinese Academy of Sciences - Chengdu, China 3Chinese Academy of Sciences - Chongqing, China ABSTRACT Smart cooking based on Amazon Echo uses the internet of things and cloud computing to assist in cooking food. People may speak to Amazon Echo during the cooking in order to get the information and situation of the cooking. Amazon Echo recognizes what people say, then transfers the information to the cloud services, and speaks to people the results that cloud services make by querying the embedded cooking knowledge and achieving the information of intelligent kitchen devices online. An intelligent food thermometer and its mobile application are well-designed and implemented to monitor the temperature of cooking food. KEYWORDS Smart Cooking, Things of Internet, Cloud Services, Smart Home. Original Source URL: ht...
Read more