Name
Abstract | ||
|---|---|---|
Most of the regression-based Deep Learning (DL) algorithms which were recently proposed are based on Convolutional Neural Networks (CNN) trained by using 2 l(2) loss function. In order to avoid the vulnerability of it to outliers, some authors propose to combine regression methods with a classification function like combination of l(2) loss function and softmax or a strategy based on bounding boxes what improves the performance of the DL neural network.In this research we propose using a combination of first order Stochastic Gradient Descent (SGD) optimizer for Artificial Neural Networks (ANNs) with Deep Learning (DL) and Deep Learning Multilayered Perceptron DLMLP with Back Propagation (BP). We focus on optimization of stochastic objectives, parameters and implementation of different activation functions in solving medical regression problems in high-dimensional space. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ner.2019.8716995 | 2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER) |
Keywords | Field | DocType |
neural networks, optimizers, backpropagation, stochastic gradient descent, momentum, Nesterov momentum, Adam, AdaMax, activation function, loss function | Computer science,Regression analysis,First order,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
1948-3546 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Natacha Gueorguieva | 1 | 63 | 12.46 |
| Iren Valova | 2 | 136 | 25.44 |
| Brian Keegan | 3 | 0 | 0.34 |