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» Maximum Likelihood Learning of Conditional MTE Distributions
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SDM
2009
SIAM
202views Data Mining» more  SDM 2009»
14 years 4 months ago
Proximity-Based Anomaly Detection Using Sparse Structure Learning.
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe,...
VLSISP
1998
111views more  VLSISP 1998»
13 years 7 months ago
Quantitative Analysis of MR Brain Image Sequences by Adaptive Self-Organizing Finite Mixtures
This paper presents an adaptive structure self-organizing finite mixture network for quantification of magnetic resonance (MR) brain image sequences. We present justification fo...
Yue Wang, Tülay Adali, Chi-Ming Lau, Sun-Yuan...
CVPR
2008
IEEE
14 years 9 months ago
Structure learning in random fields for heart motion abnormality detection
Coronary Heart Disease can be diagnosed by assessing the regional motion of the heart walls in ultrasound images of the left ventricle. Even for experts, ultrasound images are dif...
Glenn Fung, Kevin Murphy, Mark Schmidt, Róm...
ACL
2009
13 years 5 months ago
Better Word Alignments with Supervised ITG Models
This work investigates supervised word alignment methods that exploit inversion transduction grammar (ITG) constraints. We consider maximum margin and conditional likelihood objec...
Aria Haghighi, John Blitzer, John DeNero, Dan Klei...
ICIP
2000
IEEE
14 years 9 months ago
Hierarchical Image Probability (HIP) Models
We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exactly into conditional distributions of feature vecto...
Clay Spence, Lucas C. Parra, Paul Sajda