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» Graph model selection using maximum likelihood
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ICASSP
2011
IEEE
12 years 11 months ago
Maximum a posteriori based regularization parameter selection
The 1 norm regularized least square technique has been proposed as an efficient method to calculate sparse solutions. However, the choice of the regularization parameter is still...
Ashkan Panahi, Mats Viberg
SOCIALCOM
2010
13 years 5 months ago
Estimating the Size of Online Social Networks
The huge size of online social networks (OSNs) makes it prohibitively expensive to precisely measure any properties which require the knowledge of the entire graph. To estimate the...
Shaozhi Ye, Shyhtsun Felix Wu
EMNLP
2004
13 years 9 months ago
Comparing and Combining Generative and Posterior Probability Models: Some Advances in Sentence Boundary Detection in Speech
We compare and contrast two different models for detecting sentence-like units in continuous speech. The first approach uses hidden Markov sequence models based on N-grams and max...
Yang Liu, Andreas Stolcke, Elizabeth Shriberg, Mar...
BIOINFORMATICS
2007
123views more  BIOINFORMATICS 2007»
13 years 7 months ago
A comparative genome approach to marker ordering
Motivation: Genome maps are fundamental to the study of an organism and essential in the process of genome sequencing which in turn provides the ultimate map of the genome. The in...
Thomas Faraut, Simon de Givry, Patrick Chabrier, T...
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,...