Search Results for author:
Found 4 papers, 0 papers with code
Convergence Conditions of Online Regularized Statistical Learning in Reproducing Kernel Hilbert Space With Non-Stationary Data
Furthermore, if the data streams also satisfy the RKHS persistence of excitation condition, i. e. there exists a fixed length of time period, such that each eigenvalue of the conditional expectation of the operators induced by the input data accumulated over every time period has a uniformly positive lower bound with respect to time, then the output of the algorithm is consistent with the unknown function in mean square.
Random Inverse Problems Over Graphs: Decentralized Online Learning
Moreover, we propose a decentralized online learning algorithm in RKHS based on non-stationary and non-independent online data streams, and prove that the algorithm is mean square and almost surely strongly consistent if the operators induced by the random input data satisfy the infinite-dimensional spatio-temporal persistence of excitation condition.
Decentralized Online Regularized Learning Over Random Time-Varying Graphs
We study the decentralized online regularized linear regression algorithm over random time-varying graphs.
Decentralized Cooperative Online Estimation With Random Observation Matrices, Communication Graphs and Time Delays
Firstly, for the delay-free case, we show that the algorithm gains can be designed properly such that all nodes' estimates converge to the true parameter in mean square and almost surely if the observation matrices and communication graphs satisfy the stochastic spatiotemporal persistence of excitation condition.
