Graduation Semester and Year
Fall 2024
Language
English
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Electrical Engineering
First Advisor
Michael T Manry
Abstract
Interpreting multi-layer perceptron (MLP) classifier outputs as posterior probabilities is a well-established practice in machine learning and is supported in the literature. However, several authors point out that MLP outputs are very poor estimates of the posterior probabilities. This is demonstrated for classifiers with and without nonlinear output activation. Achieving this reliability depends on key factors such as model complexity, sufficient training data availability, and optimization techniques' effectiveness. In practice, these requirements are not met, resulting in suboptimal probability estimates. Our approach introduces an innovative method based on the softmax output. The method aim to refine MLP discriminants into more reliable posterior probability estimates. We validate our framework through extensive experiments on diverse real-world datasets, comparing its performance against Naive Bayesian classifiers. Our results show substantial improvements in the accuracy of probability estimates, marking a significant advance in probabilistic inference for neural network outputs.
Keywords
Neural Network, feedforward, Multiple Optimal Learning factors, Scalable, Machine Learning, Posterior Probability, Probability Estimation, Activation Functions
Disciplines
Controls and Control Theory | Signal Processing
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
harshvardhan, Harshvardhan, "NEURAL NET ESTIMATION OF DISCRIMINANTS POSTERIOR PROBABILITY VECTOR" (2024). Electrical Engineering Theses. 393.
https://mavmatrix.uta.edu/electricaleng_theses/393