We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to loca...
We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, so...
We propose a novel approach to designing algorithms for
object tracking based on fusing multiple observation models.
As the space of possible observation models is too large
for...
We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence ...
The algorithm of on-line predictor from input-output data pairs will be proposed. In this paper, it proposed an approach to generate fuzzy rules of predictor from real-time input-o...