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MOR
2006
81views more  MOR 2006»
13 years 9 months ago
Simulated Annealing for Convex Optimization
We apply the method known as simulated annealing to the following problem in convex optimization: minimize a linear function over an arbitrary convex set, where the convex set is ...
Adam Tauman Kalai, Santosh Vempala
GPEM
2007
71views more  GPEM 2007»
13 years 9 months ago
Integrating generative growth and evolutionary computation for form exploration
We present a novel means of algorithmically describing a growth process that is an extension of Lindenmayer’s Map L-systems. This growth process relies upon a set of rewrite rule...
Una-May O'Reilly, Martin Hemberg
PAMI
2008
135views more  PAMI 2008»
13 years 9 months ago
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
In this paper, we develop a new effective multiple kernel learning algorithm. First, we map the input data into m different feature spaces by m empirical kernels, where each genera...
Zhe Wang, Songcan Chen, Tingkai Sun
IEE
2007
104views more  IEE 2007»
13 years 9 months ago
Complete distributed garbage collection using DGC-consistent cuts and .NET AOP-support
: The memory management of distributed objects, when done manually, is an error-prone task. It leads to memory leaks and dangling references, causing applications to fail. Avoiding...
Luís Veiga, P. Pereira, Paulo Ferreira
NN
2006
Springer
127views Neural Networks» more  NN 2006»
13 years 9 months ago
The asymptotic equipartition property in reinforcement learning and its relation to return maximization
We discuss an important property called the asymptotic equipartition property on empirical sequences in reinforcement learning. This states that the typical set of empirical seque...
Kazunori Iwata, Kazushi Ikeda, Hideaki Sakai