Learning in Short-Time Horizons with Measurable Costs Learning in Short-Time Horizons with Measurable Costs
In this work, various machine learning algorithms are compared in the context of dynamic pricing. These algorithms include the Kalman filter, artificial neural networks, particle swarm optimization and genetic algorithms. The majority of these algorithms have been modified to handle the pricing problem. The results show that these adaptations allow the learning algorithms to handle the noisy dynamic conditions and to learn quickly.
School:Brigham Young University
School Location:USA - Utah
Source Type:Master's Thesis
Keywords:dynamic pricing machine learning particle swarm optimization genetic algortithms kalman filter artificial neural networks
Date of Publication:11/07/2006