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KDD
2004
ACM
187views Data Mining» more  KDD 2004»
16 years 4 months ago
IMMC: incremental maximum margin criterion
Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or la...
Jun Yan, Benyu Zhang, Shuicheng Yan, Qiang Yang, H...
KDD
2009
ACM
207views Data Mining» more  KDD 2009»
16 years 5 months ago
DynaMMo: mining and summarization of coevolving sequences with missing values
Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn ...
Lei Li, James McCann, Nancy S. Pollard, Christos F...
ICDCS
2006
IEEE
15 years 10 months ago
Greedy is Good: On Service Tree Placement for In-Network Stream Processing
This paper is concerned with reducing communication costs when executing distributed user tasks in a sensor network. We take a service-oriented abstraction of sensor networks, whe...
Zoë Abrams, Jie Liu
PKDD
2009
Springer
155views Data Mining» more  PKDD 2009»
15 years 11 months ago
Dynamic Factor Graphs for Time Series Modeling
Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
Piotr W. Mirowski, Yann LeCun
SDM
2007
SIAM
109views Data Mining» more  SDM 2007»
15 years 5 months ago
Segmentations with Rearrangements
Sequence segmentation is a central problem in the analysis of sequential and time-series data. In this paper we introduce and we study a novel variation to the segmentation proble...
Aristides Gionis, Evimaria Terzi