A3 Book section, Chapters in research books
Newton Method for Minimal Learning Machine (2022)


Hämäläinen, J., & Kärkkäinen, T. (2022). Newton Method for Minimal Learning Machine. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 97-108). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_7


JYU authors or editors


Publication details

All authors or editors: Hämäläinen, Joonas; Kärkkäinen, Tommi

Parent publication: Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges

Parent publication editors: Tuovinen, Tero T.; Periaux, Jacques; Neittaanmäki, Pekka

ISBN: 978-3-030-70786-6

eISBN: 978-3-030-70787-3

Journal or series: Intelligent Systems, Control and Automation: Science and Engineering

ISSN: 2213-8986

eISSN: 2213-8994

Publication year: 2022

Number in series: 76

Pages range: 97-108

Number of pages in the book: 275

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-030-70787-3_7

Publication open access: Not open

Publication channel open access:

Additional information: The CSAI 2019 Conference (Computational Science and AI in Industry: New Digital Technologies for Solving Future Societal and Economical Challenges) took place at Jyväskylä, Finland, on June 12–14, 2019.


Abstract

Minimal Learning Machine (MLM) is a distance-based supervised machine learning method for classification and regression problems. Its main advances are simple formulation and fast learning. Computing the MLM prediction in regression requires a solution to the optimization problem, which is determined by the input and output distance matrix mappings. In this paper, we propose to use the Newton method for solving this optimization problem in multi-output regression and compare the performance of this algorithm with the most popular Levenberg–Marquardt method. According to our knowledge, MLM has not been previously studied in the context of multi-output regression in the literature. In addition, we propose new initialization methods to speed up the local search of the second-order methods.


Keywords: machine learning; algorithms; mathematical optimisation


Contributing organizations


Related projects


Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 2


Last updated on 2022-19-08 at 20:18