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» Bayesian Models for Finding and Grouping Junctions
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ICCV
2009
IEEE
14 years 10 months ago
Evaluating Information Contributions of Bottom-up and Top-down Processes
This paper presents a method to quantitatively evaluate information contributions of individual bottom-up and topdown computing processes in object recognition. Our objective is...
Xiong Yang, Tianfu Wu, Song-Chun Zhu
UIC
2009
Springer
14 years 7 days ago
Mining and Visualizing Mobile Social Network Based on Bayesian Probabilistic Model
Social networking has provided powerful new ways to find people, organize groups, and share information. Recently, the potential functionalities of the ubiquitous infrastructure le...
Jun-Ki Min, Su-Hyung Jang, Sung-Bae Cho
AAAI
2010
13 years 6 months ago
A Two-Dimensional Topic-Aspect Model for Discovering Multi-Faceted Topics
This paper presents the Topic-Aspect Model (TAM), a Bayesian mixture model which jointly discovers topics and aspects. We broadly define an aspect of a document as a characteristi...
Michael Paul, Roxana Girju
EC
2006
195views ECommerce» more  EC 2006»
13 years 5 months ago
Automated Global Structure Extraction for Effective Local Building Block Processing in XCS
Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are speci...
Martin V. Butz, Martin Pelikan, Xavier Llorà...
BMCBI
2007
190views more  BMCBI 2007»
13 years 5 months ago
Discriminative motif discovery in DNA and protein sequences using the DEME algorithm
Background: Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to i...
Emma Redhead, Timothy L. Bailey