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ICIP
2006
IEEE
16 years 8 months ago
Local Discriminant Embedding with Tensor Representation
We present a subspace learning method, called Local Discriminant Embedding with Tensor representation (LDET), that addresses simultaneously the generalization and data representat...
Jian Xia, Dit-Yan Yeung, Guang Dai
CVPR
2010
IEEE
16 years 3 months ago
Unified Graph Matching in Euclidean Spaces
Graph matching is a classical problem in pattern recognition with many applications, particularly when the graphs are embedded in Euclidean spaces, as is often the case for comput...
Julian McAuley, Teofilo de Campos, Tiberio Caetano
WIRN
2005
Springer
16 years 1 days ago
Recursive Neural Networks and Graphs: Dealing with Cycles
Recursive neural networks are a powerful tool for processing structured data. According to the recursive learning paradigm, the input information consists of directed positional ac...
Monica Bianchini, Marco Gori, Lorenzo Sarti, Franc...
SODA
2008
ACM
110views Algorithms» more  SODA 2008»
15 years 8 months ago
Why simple hash functions work: exploiting the entropy in a data stream
Hashing is fundamental to many algorithms and data structures widely used in practice. For theoretical analysis of hashing, there have been two main approaches. First, one can ass...
Michael Mitzenmacher, Salil P. Vadhan
DIMVA
2008
15 years 7 months ago
Data Space Randomization
Over the past several years, US-CERT advisories, as well as most critical updates from software vendors, have been due to memory corruption vulnerabilities such as buffer overflo...
Sandeep Bhatkar, R. Sekar