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» Learning Riemannian Metrics
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SIGIR
2005
ACM
14 years 1 months ago
Text classification with kernels on the multinomial manifold
Support Vector Machines (SVMs) have been very successful in text classification. However, the intrinsic geometric structure of text data has been ignored by standard kernels commo...
Dell Zhang, Xi Chen, Wee Sun Lee
ETVC
2008
13 years 9 months ago
Discrete Curvature Flows for Surfaces and 3-Manifolds
Intrinsic curvature flows can be used to design Riemannian metrics by prescribed curvatures. This chapter presents three discrete curvature flow methods that are recently introduce...
Xiaotian Yin, Miao Jin, Feng Luo 0002, Xianfeng Da...
ICONIP
2007
13 years 9 months ago
Natural Conjugate Gradient in Variational Inference
Variational methods for approximate inference in machine learning often adapt a parametric probability distribution to optimize a given objective function. This view is especially ...
Antti Honkela, Matti Tornio, Tapani Raiko, Juha Ka...
SDM
2009
SIAM
154views Data Mining» more  SDM 2009»
14 years 4 months ago
AMORI: A Metric-Based One Rule Inducer.
The requirements of real-world data mining problems vary extensively. It is plausible to assume that some of these requirements can be expressed as application-specific performan...
Niklas Lavesson, Paul Davidsson
SDL
2007
152views Hardware» more  SDL 2007»
13 years 9 months ago
TTCN-3 Quality Engineering: Using Learning Techniques to Evaluate Metric Sets
Software metrics are an essential means to assess software quality. For the assessment of software quality, typically sets of complementing metrics are used since individual metric...
Edith Werner, Jens Grabowski, Helmut Neukirchen, N...