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FLAIRS
2004
15 years 2 months ago
Clustering Spatial Data in the Presence of Obstacles
Clustering is a form of unsupervised machine learning. In this paper, we proposed the DBRS_O method to identify clusters in the presence of intersected obstacles. Without doing an...
Xin Wang, Howard J. Hamilton
EMNLP
2010
14 years 10 months ago
Unsupervised Parse Selection for HPSG
Parser disambiguation with precision grammars generally takes place via statistical ranking of the parse yield of the grammar using a supervised parse selection model. In the stan...
Rebecca Dridan, Timothy Baldwin
ACL
2008
15 years 2 months ago
Semi-Supervised Convex Training for Dependency Parsing
We present a novel semi-supervised training algorithm for learning dependency parsers. By combining a supervised large margin loss with an unsupervised least squares loss, a discr...
Qin Iris Wang, Dale Schuurmans, Dekang Lin
AVSS
2007
IEEE
15 years 7 months ago
Vehicular traffic density estimation via statistical methods with automated state learning
This paper proposes a novel approach of combining an unsupervised clustering scheme called AutoClass with Hidden Markov Models (HMMs) to determine the traffic density state in a R...
Evan Tan, Jing Chen
147
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TIP
2010
182views more  TIP 2010»
14 years 7 months ago
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changsh...