Date of Award
2019
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Khoshgoftaar, Taghi M.
Abstract
Machine learning techniques such as deep neural networks have become an indispensable tool for a wide range of applications such as image classification, speech recognition, and sentiment analysis in text. An activation function is a mathematical equation that determines the output of each neuron in the neural network. In deep learning architectures the choice of activation functions is very important to the network’s performance. Activation functions determine the output of the model, its computational efficiency, and its ability to train and converge after multiple iterations of training epochs. The selection of an activation function is critical to building and training an effective and efficient neural network. In real-world applications of deep neural networks, the activation function is a hyperparameter. We have observed a lack of consensus on how to select a good activation function for a deep neural network, and that a specific function may not be suitable for all domain-specific applications.
Extent
233 p.
Recommended Citation
Castaneda, Gabriel, "DEEP MAXOUT NETWORKS FOR CLASSIFICATION PROBLEMS ACROSS MULTIPLE DOMAINS" (2019). Electronic Theses and Dissertations 2010 - 2019. 2141.
https://digitalcommons.fau.edu/etd_general_6/2141
Identifier
FA00013362
Comments
Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2019.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.