Distributed Shape Learning of Complex Objects Using Gaussian Kernel

Autor:	Oshima, Toshiyuki, Yamauchi, Junya, Ibuki, Tatsuya, Seto, Michio, Hatanaka, Takeshi
Rok vydání:	2024
Předmět:	Computer Science - Robotics Electrical Engineering and Systems Science - Systems and Control Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	This paper addresses distributed learning of a complex object for multiple networked robots based on distributed optimization and kernel-based support vector machine. In order to overcome a fundamental limitation of polynomial kernels assumed in our antecessor, we employ Gaussian kernel as a kernel function for classification. The Gaussian kernel prohibits the robots to share the function through a finite number of equality constraints due to its infinite dimensionality of the function space. We thus reformulate the optimization problem assuming that the target function space is identified with the space spanned by the bases associated with not the data but a finite number of grid points. The above relaxation is shown to allow the robots to share the function by a finite number of equality constraints. We finally demonstrate the present approach through numerical simulations. Comment: 4 pages, 5 figures, Discussion paper, 22nd IFAC World Congress, pp. 2475-2478, 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.10916 Zobrazit plný text záznamu View this record from Arxiv