Paper Info
Title
Active EM to reduce noise in activity recognition
Abstract
Intelligent desktop environments allow the desktop user to define a set of projects or activities that characterize the user's desktop work. These environments then attempt to identify the current activity of the user in order to provide various kinds of assistance. These systems take a hybrid approach in which they allow the user to declare their current activity but they also employ learned classifiers to predict the current activity to cover those cases where the user forgets to declare the current activity. The classifiers must be trained on the very noisy data obtained from the user's activity declarations. Instead of asking the user to review and relabel the data manually, we employ an active EM algorithm that combines the EM algorithm and active learning. EM can be viewed as retraining on its own predictions. To make it more robust, we only retrain on those predictions that are made with high confidence. For active learning, we make a small number of queries to the user based on the most uncertain instances. Experimental results on real users show this active EM algorithm can significantly improve the prediction precision, and that it performs better than either EM or active learning alone.
Year
DOI
Venue
2007
10.1145/1216295.1216323
IUI
Keywords
Field
DocType
active em,activity declaration,real user,desktop work,active em algorithm,active learning,noisy data,current activity,intelligent desktop environment,activity recognition,em algorithm,desktop user,noise,expectation maximization,machine learning
Data mining,Noisy data,Active learning,Activity recognition,Active learning (machine learning),Intelligent interface,Computer science,Expectation–maximization algorithm,Artificial intelligence,Retraining,Machine learning
Conference
ISBN
Citations 
PageRank 
1-59593-481-2
2
0.46
References 
Authors
22
2
Name
Order
Citations
PageRank
Jianqiang Shen123617.86
Thomas G. Dietterich293361722.57