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» Learning from Highly Structured Data by Decomposition
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SDM
2011
SIAM
232views Data Mining» more  SDM 2011»
14 years 26 days ago
A Sequential Dual Method for Structural SVMs
In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to computationa...
Shirish Krishnaj Shevade, Balamurugan P., S. Sunda...
ICDM
2003
IEEE
136views Data Mining» more  ICDM 2003»
15 years 3 months ago
Statistical Relational Learning for Document Mining
A major obstacle to fully integrated deployment of many data mining algorithms is the assumption that data sits in a single table, even though most real-world databases have compl...
Alexandrin Popescul, Lyle H. Ungar, Steve Lawrence...
HIPC
2009
Springer
14 years 7 months ago
Highly scalable algorithm for distributed real-time text indexing
Stream computing research is moving from terascale to petascale levels. It aims to rapidly analyze data as it streams in from many sources and make decisions with high speed and a...
Ankur Narang, Vikas Agarwal, Monu Kedia, Vijay K. ...
ICML
2007
IEEE
15 years 11 months ago
Hierarchical Gaussian process latent variable models
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
Neil D. Lawrence, Andrew J. Moore
PERCOM
2009
ACM
15 years 10 months ago
High Accuracy Context Recovery using Clustering Mechanisms
This paper examines the recovery of user context in indoor environments with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extra...
Dinh Q. Phung, Brett Adams, Kha Tran, Svetha Venka...