Proceedings of the sixth international workshop on Machine learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An online-optimized incremental learning framework for video semantic classification
Proceedings of the 12th annual ACM international conference on Multimedia
StreamMiner: a classifier ensemble-based engine to mine concept-drifting data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Semi-supervised learning for semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Adapting appearance models of semantic concepts to particular videos via transductive learning
Proceedings of the international workshop on Workshop on multimedia information retrieval
Robust Video Content Analysis via Transductive Learning
ACM Transactions on Intelligent Systems and Technology (TIST)
Recentness biased learning for time series forecasting
Information Sciences: an International Journal
| Hi-index | 0.00 |
Concept drifting is an important and challenging research issue in the field of machine learning. This paper mainly addresses the issue of semantic concept drifting in time series such as video streams over a relatively long period of time. An Online-Optimized Incremental Learning framework is proposed as an example learning system for tracking the drifting concepts. Furthermore, a set of measures are defined to track the process of concept drifting in the learning system. These tracking measures are also applied to determine the corresponding parameters used for model updating in order to obtain the optimal up-to-date classifiers. Experiments on the data set of TREC Video Retrieval Evaluation 2004 not only demonstrate the inside concept drifting process of the learning system, but also prove that the proposed learning framework is promising for tackling the issue of concept drifting.