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» Online Data Mining for Co-Evolving Time Sequences
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CVPR
2011
IEEE
14 years 6 months ago
Extracting and Locating Temporal Motifs in Video Scenes Using a Hierarchical Non Parametric Bayesian Model
In this paper, we present an unsupervised method for mining activities in videos. From unlabeled video sequences of a scene, our method can automatically recover what are the recu...
Ré, mi Emonet, Jagannadan Varadarajan, Jean-Marc ...
CORR
2010
Springer
186views Education» more  CORR 2010»
14 years 10 months ago
Significant Interval and Frequent Pattern Discovery in Web Log Data
There is a considerable body of work on sequence mining of Web Log Data We are using One Pass frequent Episode discovery (or FED) algorithm, takes a different approach than the tr...
Kanak Saxena, Rahul Shukla
SDM
2009
SIAM
119views Data Mining» more  SDM 2009»
15 years 7 months ago
Twin Vector Machines for Online Learning on a Budget.
This paper proposes Twin Vector Machine (TVM), a constant space and sublinear time Support Vector Machine (SVM) algorithm for online learning. TVM achieves its favorable scaling b...
Zhuang Wang, Slobodan Vucetic
EDBT
2010
ACM
170views Database» more  EDBT 2010»
15 years 1 months ago
Augmenting OLAP exploration with dynamic advanced analytics
Online Analytical Processing (OLAP) is a popular technique for explorative data analysis. Usually, a fixed set of dimensions (such as time, place, etc.) is used to explore and ana...
Benjamin Leonhardi, Bernhard Mitschang, Rubé...
ICRA
2008
IEEE
191views Robotics» more  ICRA 2008»
15 years 4 months ago
Combining automated on-line segmentation and incremental clustering for whole body motions
Abstract— This paper describes a novel approach for incremental learning of human motion pattern primitives through on-line observation of human motion. The observed motion time ...
Dana Kulic, Wataru Takano, Yoshihiko Nakamura