Kernel-Based Regressors Equivalent to Stochastic Affine Estimators-Reference-Cited by-同舟云学术

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Kernel-Based Regressors Equivalent to Stochastic Affine Estimators

Published:2022-01-01 Issue:1 Volume:E105.D Page:116-122
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

TANAKA Akira¹,NAKAMURA Masanari¹,IMAI Hideyuki¹

Affiliation:

1. Division of Computer Science and Information Technology, Faculty of Information Science and Technology, Hokkaido University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E105.D/1/E105.D_2021EDP7156/_pdf

Reference13 articles.

1. [3] K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, “An introduction to kernel-based learning algorithms,” IEEE Trans. Neural Netw., vol.12, no.2, pp.181-201, 2001. 10.1109/72.914517

2. [4] N. Aronszajn, “Theory of reproducing kernels,” Trans. American Mathematical Society, vol.68, no.3, pp.337-404, 1950. 10.1090/s0002-9947-1950-0051437-7

3. [5] A. Tanaka and H. Imai, “Kernel ridge regression with autocorrelation prior: Optimal model and cross-validation,” 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2020), pp.3867-3871, 2020. 10.1109/icassp40776.2020.9053423

4. [6] T. Kailath, A.H. Sayed, and B. Hassibi, Linear Estimation, Prentice-Hall, Upper Saddle River, NJ, 2000.

5. [7] C.E. Rasmussen, “Gaussian processes in machine learning,” Summer School on Machine Learning, Lecture Notes in Computer Science, vol.3176, pp.63-71, Springer 2004. 10.1007/978-3-540-28650-9_4

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