Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning

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Learning Theory for Distribution Regression (2014, background): “, 2013), personalized human activity recognition based on iPod/iPhone accelerometer data (Sun et al., 2013), finger trajectory prediction in brain-computer interfaces (Kadri et al.”
Asynchronous Online Federated Learning for Edge Devices (2019, background): “Previous online learning approaches [14, 15, 17] with multiple clients are not capable of solving the aforementioned challenges because these approaches share training samples of each client with other clients; which has the same privacy concern as centralized data centers.”
Collaborating between Local and Global Learning for Distributed Online Multiple Tasks (2015, methodology): “Previous online multi-task learning methods [6, 30] can not be used for these problems because in the learning process of these methods, one task must use the raw training samples from all the other tasks, which is not feasible in our problem.”
Online Multi-Task Learning Framework for Ensemble Forecasting (2017, unknown): “[39] employs a stochastic gradient descent approach to solve its multi-task learning problem.”
Structure Regularization for Structured Prediction (2014, methodology): “This is a task based on real-valued sensor signals, with the data extracted from the Bao04 activity recognition dataset [15].”