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JCB
2002
70views more  JCB 2002»
14 years 11 months ago
Strong Feature Sets from Small Samples
For small samples, classi er design algorithms typically suffer from over tting. Given a set of features, a classi er must be designed and its error estimated. For small samples, ...
Seungchan Kim, Edward R. Dougherty, Junior Barrera...
HICSS
2006
IEEE
152views Biometrics» more  HICSS 2006»
15 years 5 months ago
Distributed Uniform Sampling in Unstructured Peer-to-Peer Networks
— Uniform sampling in networks is at the core of a wide variety of randomized algorithms. Random sampling can be performed by modeling the system as an undirected graph with asso...
Asad Awan, Ronaldo A. Ferreira, Suresh Jagannathan...
CGF
2005
204views more  CGF 2005»
14 years 11 months ago
BRDF and geometry capture from extended inhomogeneous samples using flash photography
We present a technique which allows capture of 3D surface geometry and a useful class of BRDFs using extremely simple equipment. A standard digital camera with an attached flash s...
James A. Paterson, David Claus, Andrew W. Fitzgibb...
SDM
2009
SIAM
138views Data Mining» more  SDM 2009»
15 years 9 months ago
ShatterPlots: Fast Tools for Mining Large Graphs.
Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A deep, recurring...
Ana Paula Appel, Andrew Tomkins, Christos Faloutso...
KDD
2007
ACM
227views Data Mining» more  KDD 2007»
16 years 3 days ago
Fast best-effort pattern matching in large attributed graphs
We focus on large graphs where nodes have attributes, such as a social network where the nodes are labelled with each person's job title. In such a setting, we want to find s...
Hanghang Tong, Christos Faloutsos, Brian Gallagher...