Optimization and Generalization Dynamics in Multi-Layer Perceptron Classifiers for Low-Dimensional Feature Embeddings

Author(s)	Arnab Sen
Country	India
Abstract	This study systematically investigates the critical relationship between architectural complexity, advanced optimization techniques, and regularization mechanisms in the development of robust Multi-Layer Perceptron (MLP) models tailored for feature classification tasks. The model architecture employed utilized the Keras deep learning framework, constructed from a sequential cascade of Dense layers and non-linear Rectified Linear Unit (ReLU) activations, culminating in a SoftMax classification layer for probabilistic output estimation.
Keywords	Multi-Layer Perceptron, Keras, Adam Optimizer, Hyperparameter Tuning, Overfitting Mitigation, Dropout, Weight Decay, Early Stopping
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 6, November-December 2025
Published On	2025-11-15
DOI	https://doi.org/10.63363/aijfr.2025.v06i06.2011
Short DOI	https://doi.org/hbbz8k

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