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» Graph model selection using maximum likelihood
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TIT
2010
170views Education» more  TIT 2010»
13 years 2 months ago
Belief propagation estimation of protein and domain interactions using the sum-product algorithm
We present a novel framework to estimate protein-protein (PPI) and domain-domain (DDI) interactions based on a belief propagation estimation method that efficiently computes inter...
Faruck Morcos, Marcin Sikora, Mark S. Alber, Dale ...
NLPRS
2001
Springer
14 years 1 days ago
Named Entity Recognition using Machine Learning Methods and Pattern-Selection Rules
Named Entity recognition, as a task of providing important semantic information, is a critical first step in Information Extraction and QuestionAnswering system. This paper propos...
Choong-Nyoung Seon, Youngjoong Ko, Jeong-Seok Kim,...
PAMI
2006
143views more  PAMI 2006»
13 years 7 months ago
Variational Bayes for Continuous Hidden Markov Models and Its Application to Active Learning
In this paper we present a variational Bayes (VB) framework for learning continuous hidden Markov models (CHMMs), and we examine the VB framework within active learning. Unlike a ...
Shihao Ji, Balaji Krishnapuram, Lawrence Carin
CORR
2007
Springer
135views Education» more  CORR 2007»
13 years 7 months ago
Detailed Network Measurements Using Sparse Graph Counters: The Theory
— Measuring network flow sizes is important for tasks like accounting/billing, network forensics and security. Per-flow accounting is considered hard because it requires that m...
Yi Lu, Andrea Montanari, Balaji Prabhakar
ER
2007
Springer
101views Database» more  ER 2007»
14 years 1 months ago
Using Attributed Goal Graphs for Software Component Selection: An Application of Goal-Oriented Analysis to Decision Making
During software requirements analysis and design steps, developers and stakeholders have many alternatives of artifacts such as software component selection and should make decisi...
Kazuma Yamamoto, Motoshi Saeki