@inproceedings{e5bba6eaff15400abefcba02feb21bdb,
title = "Convolutional Neural Network Architecture Design by the Tree Growth Algorithm Framework",
abstract = "This paper presents tree growth algorithm framework for designing convolutional neural network architecture. Convolutional neural networks are a special class of deep neural networks that typically consist of several convolution, pooling and fully connected layers. Convolutional neural networks have proved to be a robust method for tackling various image classification tasks. One of the most important challenges from this domain is to find the network architecture that has the best performance for the specific application. The performance of the network depends on the set of hyper-parameter values such as the number of convolutional and dense layers, the number of kernels per layer and kernel size. Optimization of hyperparameters was performed by novel tree growth algorithm that belongs to the group of swarm intelligence metaheuristics. The robustness, performance and solutions quality of the proposed framework was validated against the well-known MNIST dataset. Conducted comparative analysis demonstrated that the proposed frameworks obtains promising results in this domain.",
keywords = "covolutional neural networks, hyper-parameters, optimization, swarm intelligence, tree growth algorithm",
author = "Ivana Strumberger and Eva Tuba and Nebojsa Bacanin and Raka Jovanovic and Milan Tuba",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Joint Conference on Neural Networks, IJCNN 2019 ; Conference date: 14-07-2019 Through 19-07-2019",
year = "2019",
month = jul,
doi = "10.1109/IJCNN.2019.8851755",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 International Joint Conference on Neural Networks, IJCNN 2019",
address = "United States",
}