@inproceedings{da39f2f3ded04bb1880c975ea5185376,
title = "Using a simulated annealing to enhance learning in adjustment processes",
abstract = "This paper introduces a new approach to enhance learning in adjustment processes by using a support vector machine (SVM) algorithm as discriminant function jointly with an action generator module. The method trains a SVM with state-action patterns and uses trained SVM to select an appropriate action given a certain state in order to reach the target state. The system incorporates a simulated annealing technique to increase the exploration capacity and improve the ability to avoid local minima. The methodology has been tested in an example with artificial data.",
keywords = "Adjustment process, Learning Algorithms, Machine Learning, Simulated Annealing, Support Vector Machines",
author = "Albert Sam{\`a} and Ruiz, {Francisco J.} and N. Agell and Cecilio Angulo",
year = "2009",
doi = "10.3233/978-1-60750-061-2-119",
language = "English",
isbn = "9781607500612",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
number = "1",
pages = "119--127",
booktitle = "Frontiers in Artificial Intelligence and Applications",
address = "Netherlands",
edition = "1",
}