Abstract
The Minimal Learning Machine (MLM) Has Been Recently Proposed as a Novel Supervised Learning Method for Regression Problems Aiming at Reconstructing the Mapping between Input and Output Distance Matrices. Estimation of the Response is Then Achieved from the Geometrical Configuration of the Output Points. Thanks to its Comprehensive Formulation, the MLM is Inherently Capable of Dealing with Nonlinear Problems and Multidimensional Output Spaces. in This Paper, We Introduce an Extension of the MLM to Classification Tasks, Thus Providing a Unified Framework for Mult response Regression and Classification Problems. on the Basis of Our Experiments, the MLM Achieves Results that Are Comparable to Many De Facto Standard Methods for Classification with the Advantage of Offering a Computationally Lighter Alternative to Such Approaches. © 2013 Ieee.
Recommended Citation
A. H. Junior et al., "Extending the Minimal Learning Machine for Pattern Classification," Proceedings - 1st BRICS Countries Congress on Computational Intelligence, BRICS-CCI 2013, pp. 236 - 241, article no. 6855855, Institute of Electrical and Electronics Engineers, Jan 2013.
The definitive version is available at https://doi.org/10.1109/BRICS-CCI-CBIC.2013.46
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-147993194-1
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
01 Jan 2013
