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ICML
2010
IEEE
13 years 11 months ago
Gaussian Process Change Point Models
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...
Yunus Saatci, Ryan Turner, Carl Edward Rasmussen
KDD
2010
ACM
218views Data Mining» more  KDD 2010»
14 years 2 months ago
Online multiscale dynamic topic models
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...
Tomoharu Iwata, Takeshi Yamada, Yasushi Sakurai, N...
CVPR
2009
IEEE
15 years 5 months ago
Learning to Track with Multiple Observers
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...
Björn Stenger, Roberto Cipolla, Thomas Woodle...
ICML
2005
IEEE
14 years 11 months ago
Using additive expert ensembles to cope with concept drift
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 ...
Jeremy Z. Kolter, Marcus A. Maloof
FUZZIEEE
2007
IEEE
14 years 4 months ago
An On-Line Fuzzy Predictor from Real-Time Data
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...
Chih-Ching Hsiao, Shun-Feng Su