Optimizing neural network using clustering for resource constrained environment

Chong Tian; Danda B. Rawat

doi:10.1117/12.3014231

7 June 2024 Optimizing neural network using clustering for resource constrained environment

Chong Tian, Danda B. Rawat

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 130511L (2024) https://doi.org/10.1117/12.3014231
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

Machine learning is a pervasive technique in contemporary applications, representing a subfield of artificial intelligence dedicated to machines emulating human behaviors. Neural networks, a prominent class of machine learning models, excel in decision-making tasks. Nevertheless, the empirical nature of designing a neural network structure poses challenges, with practitioners often facing the dilemma of incorporating excessive neurons, leading to prolonged training times, or insufficient neurons, resulting in training failures. This paper presents a solution by introducing a method that recommends an appropriate range of neuron numbers for a neural network, leveraging clustering methods to enhance structural design efficiency.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chong Tian and Danda B. Rawat "Optimizing neural network using clustering for resource constrained environment", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 130511L (7 June 2024); https://doi.org/10.1117/12.3014231

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