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...
This paper considers the problems of feature variation and concept uncertainty in typical learning-based video semantic classification schemes. We proposed a new online semantic c...
We have recently introduced an incremental learning algorithm, Learn++ .NSE, for Non-Stationary Environments, where the data distribution changes over time due to concept drift. Le...
Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any online learner for co...
Significant appearance changes of objects under different orientations could cause loss of tracking, "drifting." In this paper, we present a collaborative tracking framew...