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» Splitting of Learnable Classes
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CCR
2008
88views more  CCR 2008»
13 years 8 months ago
Rethinking virtual network embedding: substrate support for path splitting and migration
Network virtualization is a powerful way to run multiple architectures or experiments simultaneously on a shared infrastructure. However, making efficient use of the underlying re...
Minlan Yu, Yung Yi, Jennifer Rexford, Mung Chiang
ICPR
2008
IEEE
14 years 3 months ago
Finding the splitting vector for image resolution up-conversion
This paper proposes a new single-frame image upconversion approach that uses prior information. The proposed method overcomes the drawbacks of the Kondo 2001 where the class membe...
Xinyu Xu, Hao Pan
ICANNGA
2007
Springer
100views Algorithms» more  ICANNGA 2007»
14 years 15 days ago
Softening Splits in Decision Trees Using Simulated Annealing
Predictions computed by a classification tree are usually constant on axis-parallel hyperrectangles corresponding to the leaves and have strict jumps on their boundaries. The densi...
Jakub Dvorák, Petr Savický
ALT
1995
Springer
14 years 6 days ago
Learning Unions of Tree Patterns Using Queries
This paper characterizes the polynomial time learnability of TPk, the class of collections of at most k rst-order terms. A collection in TPk de nes the union of the languages de n...
Hiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara
CAIP
2009
Springer
114views Image Analysis» more  CAIP 2009»
13 years 12 months ago
Decision Trees Using the Minimum Entropy-of-Error Principle
Binary decision trees based on univariate splits have traditionally employed so-called impurity functions as a means of searching for the best node splits. Such functions use estim...
Joaquim Marques de Sá, João Gama, Ra...