SS-MLDA-1.4

REAL TIME KERNEL LEARNING FOR SENSOR NETWORKS USING PRINCIPLES OF FEDERATED LEARNING

Anthony Kuh, University of Hawaii, United States of America
Session:
Online and Distributed Kernel Learning Algorithms
Track:
Machine Learning and Data Analytics (MLDA)
Session Time:
Thu, 16 Dec, 09:00 - 11:00 Japan Standard Time (UTC +9)
Thu, 16 Dec, 00:00 - 02:00 Coordinated Universal Time
Wed, 15 Dec, 19:00 - 21:00 Eastern Standard Time (UTC -5)
Wed, 15 Dec, 16:00 - 18:00 Pacific Standard Time (UTC -8)
Session Co-Chairs:
Anthony Kuh, University of Hawaii and Masahiro Yukawa, Keio University and Yuichi Tanaka, Tokyo University of Agriculture and Technology
Presentation
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Session SS-MLDA-1
TH1.LS-C.1: Graph Kernel Recursive Least-Squares Algorithms
Vinay Chakravarthi gogineni, Valeriya Naumova, Simula Metropolitan Center for Digital Engineering, Norway; Stefan Werner, Norwegian University of Science and Technology, Norway; Yih-Fang Huang, University of Notre Dame, United States of America
TH1.LS-C.2: A Hilbertian Projection Approach with Dictionary Dividing Strategy: Accelerating Nonlinear Estimation Algorithm with Multiscale Gaussians
Masaaki Takizawa, National Institute of Technology, Toyama College, Japan; Masahiro Yukawa, Keio University, Japan
TH1.LS-C.3: PERSONALIZED LEARNING USING MULTIPLE KERNEL MODELS
Anthony Kuh, University of Hawaii, United States of America; Shuai Huang, Cynthia Chen, University of Washington, United States of America
TH1.LS-C.4: REAL TIME KERNEL LEARNING FOR SENSOR NETWORKS USING PRINCIPLES OF FEDERATED LEARNING
Anthony Kuh, University of Hawaii, United States of America