Minimal Learning Machine: A New Distance-Based Method for Supervised Learning
Abstract
In This Work, a Novel Supervised Learning Method, the Minimal Learning Machine (Mlm), is Proposed. Learning a Mlm Consists in Reconstructing the Mapping Existing between Input and Output Distance Matrices and Then Estimating the Response from the Geometrical Configuration of the Output Points. Given its General Formulation, the Minimal Learning Machine is Inherently Capable to Operate on Nonlinear Regression Problems as Well as on Multidimensional Response Spaces. in Addition, an Intuitive Extension of the Mlm is Proposed to Deal with Classification Problems. on the Basis of Our Experiments, the Minimal Learning Machine is Able to Achieve Accuracies that Are Comparable to Many De Facto Standard Methods for Regression and It Offers a Computationally Valid Alternative to Such Approaches. © 2013 Springer-Verlag Berlin Heidelberg.
Recommended Citation
A. H. De Souza Junior et al., "Minimal Learning Machine: A New Distance-Based Method for Supervised Learning," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7902 LNCS, no. PART 1, pp. 408 - 416, Springer, Jul 2013.
The definitive version is available at https://doi.org/10.1007/978-3-642-38679-4_40
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-364238678-7
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
17 Jul 2013
