Abstract
The Paper Proposes a Methodology Called OO-KNN, Which Builds a One Hidden-Layer Feedforward Neural Network, using Nearest Neighbors Neurons with Extremely Small Computational Time. the Main Strategy is to Select the Most Relevant Variables Beforehand, Then to Build the Model using KNN Kernels. Multi response Sparse Regression (MRSR) is Used as the Second Step in Order to Rank Each Kth Nearest Neighbor and Finally as a Third Step Leave-One-Out Estimation is Used to Select the Number of Neighbors and to Estimate the Generalization Performances. This New Methodology is Tested on a Toy Example and is Applied to Financial Modeling. © 2008 IEEE.
Recommended Citation
Q. Yu et al., "Optimal Pruned K-Nearest Neighbors: OP-KNN - Application to Financial Modeling," Proceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008, pp. 764 - 769, article no. 4626723, Institute of Electrical and Electronics Engineers, Nov 2008.
The definitive version is available at https://doi.org/10.1109/HIS.2008.134
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-076953326-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
10 Nov 2008
