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NIPS
2004
13 years 11 months ago
Proximity Graphs for Clustering and Manifold Learning
Many machine learning algorithms for clustering or dimensionality reduction take as input a cloud of points in Euclidean space, and construct a graph with the input data points as...
Miguel Á. Carreira-Perpiñán, ...
ICCV
2011
IEEE
12 years 9 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
METRICS
2002
IEEE
14 years 2 months ago
What We Have Learned About Fighting Defects
The Center for Empirically Based Software Engineering helps improve software development by providing guidelines for selecting development techniques, recommending areas for furth...
Forrest Shull, Victor R. Basili, Barry W. Boehm, A...
TMI
2008
154views more  TMI 2008»
13 years 9 months ago
Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models
In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discrimin...
Zhuowen Tu, Katherine Narr, Piotr Dollár, I...
INFOCOM
2007
IEEE
14 years 4 months ago
Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares
— High-speed backbones are regularly affected by various kinds of network anomalies, ranging from malicious attacks to harmless large data transfers. Different types of anomalies...
Tarem Ahmed, Mark Coates, Anukool Lakhina