<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Incremental Learning on</title><link>https://www.rebeccaadaimi.com/categories/incremental-learning/</link><description>Recent content in Incremental Learning on</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Fri, 11 Mar 2022 00:00:00 +0000</lastBuildDate><atom:link href="https://www.rebeccaadaimi.com/categories/incremental-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks</title><link>https://www.rebeccaadaimi.com/publications/lapnet-har/</link><pubDate>Fri, 11 Mar 2022 00:00:00 +0000</pubDate><guid>https://www.rebeccaadaimi.com/publications/lapnet-har/</guid><description>Continual learning, also known as lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-world applications, an essential step towards the long-term deployment of such recognition systems is to extend the activity model to dynamically adapt to changes in people&amp;rsquo;s everyday behavior. Current research in continual learning applied to HAR domain is still under-explored with researchers exploring existing methods developed for computer vision in HAR. Moreover, analysis has so far focused on task-incremental or class-incremental learning paradigms where task boundaries are known. This impedes the applicability of such methods for real-world systems since data is presented in a randomly streaming fashion. To push this field forward, we build on recent advances in the area of continual machine learning and design a lifelong adaptive learning framework using Prototypical Networks&amp;hellip;</description></item></channel></rss>