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» Approximate algorithms for neural-Bayesian approaches
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NLP
2000
15 years 9 months ago
Monte-Carlo Sampling for NP-Hard Maximization Problems in the Framework of Weighted Parsing
Abstract. The purpose of this paper is (1) to provide a theoretical justification for the use of Monte-Carlo sampling for approximate resolution of NP-hard maximization problems in...
Jean-Cédric Chappelier, Martin Rajman
ICCV
2005
IEEE
16 years 8 months ago
Quasiconvex Optimization for Robust Geometric Reconstruction
Geometric reconstruction problems in computer vision are often solved by minimizing a cost function that combines the reprojection errors in the 2D images. In this paper, we show t...
Qifa Ke, Takeo Kanade
ICPR
2006
IEEE
16 years 7 months ago
Multiple Objects Tracking with Multiple Hypotheses Graph Representation
We present a novel multi-object tracking algorithm based on multiple hypotheses about the trajectories of the objects. Our work is inspired by Reid's multiple hypothesis trac...
Alex Yong Sang Chia, Liyuan Li, Weimin Huang
ICML
2008
IEEE
16 years 6 months ago
Active kernel learning
Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. A variety of kernel learning algorithms have been prop...
Steven C. H. Hoi, Rong Jin
KDD
2008
ACM
178views Data Mining» more  KDD 2008»
16 years 6 months ago
iSAX: indexing and mining terabyte sized time series
Current research in indexing and mining time series data has produced many interesting algorithms and representations. However, it has not led to algorithms that can scale to the ...
Jin Shieh, Eamonn J. Keogh