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» Supervised Feature Extraction Using Hilbert-Schmidt Norms
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JMLR
2011
133views more  JMLR 2011»
14 years 4 months ago
Operator Norm Convergence of Spectral Clustering on Level Sets
Following Hartigan (1975), a cluster is defined as a connected component of the t-level set of the underlying density, that is, the set of points for which the density is greater...
Bruno Pelletier, Pierre Pudlo
NAACL
2010
14 years 7 months ago
Minimally-Supervised Extraction of Entities from Text Advertisements
Extraction of entities from ad creatives is an important problem that can benefit many computational advertising tasks. Supervised and semi-supervised solutions rely on labeled da...
Sameer Singh, Dustin Hillard, Chris Leggetter
ICAISC
2010
Springer
14 years 11 months ago
Canonical Correlation Analysis for Multiview Semisupervised Feature Extraction
Hotelling’s Canonical Correlation Analysis (CCA) works with two sets of related variables, also called views, and its goal is to find their linear projections with maximal mutual...
Olcay Kursun, Ethem Alpaydin
81
Voted
CSL
2010
Springer
14 years 9 months ago
Improving supervised learning for meeting summarization using sampling and regression
Meeting summarization provides a concise and informative summary for the lengthy meetings and is an effective tool for efficient information access. In this paper, we focus on ext...
Shasha Xie, Yang Liu
91
Voted
ICPR
2006
IEEE
15 years 10 months ago
Vessel Segmentation in 2D-Projection Images Using a Supervised Linear Hysteresis Classifier
2D projection imaging is a widely used procedure for vessel visualization. For the subsequent analysis of the vasculature, precise measurements of e.g. vessel area, vessel length ...
Alexandru Condurache, Til Aach