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» On the Learnability of Vector Spaces
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3DPVT
2004
IEEE
316views Visualization» more  3DPVT 2004»
15 years 1 months ago
A Statistical Method for Robust 3D Surface Reconstruction from Sparse Data
Abstract-General information about a class of objects, such as human faces or teeth, can help to solve the otherwise ill-posed problem of reconstructing a complete surface from spa...
Volker Blanz, Albert Mehl, Thomas Vetter, Hans-Pet...
JAC
2008
14 years 11 months ago
Quantization of cellular automata
Take a cellular automaton, consider that each configuration is a basis vector in some vector space, and linearize the global evolution function. If lucky, the result could actually...
Pablo Arrighi, Vincent Nesme
BMVC
2000
14 years 11 months ago
Recognising the Dynamics of Faces across Multiple Views
We present an integrated framework for dynamic face detection and recognition, where head pose is estimated using Support Vector Regression, face detection is performed by Support...
Yongmin Li, Shaogang Gong, Heather M. Liddell
98
Voted
CCGRID
2010
IEEE
14 years 11 months ago
High Performance Dimension Reduction and Visualization for Large High-Dimensional Data Analysis
Abstract--Large high dimension datasets are of growing importance in many fields and it is important to be able to visualize them for understanding the results of data mining appro...
Jong Youl Choi, Seung-Hee Bae, Xiaohong Qiu, Geoff...
91
Voted
CORR
2006
Springer
130views Education» more  CORR 2006»
14 years 10 months ago
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
Christian Gagné, Marc Schoenauer, Mich&egra...