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» Learning Recursive Control Programs from Problem Solving
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ICML
2005
IEEE
14 years 8 months ago
Analysis and extension of spectral methods for nonlinear dimensionality reduction
Many unsupervised algorithms for nonlinear dimensionality reduction, such as locally linear embedding (LLE) and Laplacian eigenmaps, are derived from the spectral decompositions o...
Fei Sha, Lawrence K. Saul
TON
2010
118views more  TON 2010»
13 years 5 months ago
ILP formulations for p-cycle design without candidate cycle enumeration
—The concept of p-cycle (preconfigured protection cycle) allows fast and efficient span protection in wavelength division multiplexing (WDM) mesh networks. To design p-cycles f...
Bin Wu, Kwan L. Yeung, Pin-Han Ho
ICCV
2009
IEEE
15 years 12 days ago
Constrained Clustering by Spectral Kernel Learning
Clustering performance can often be greatly improved by leveraging side information. In this paper, we consider constrained clustering with pairwise constraints, which specify s...
Zhenguo Li, Jianzhuang Liu
GECCO
2010
Springer
158views Optimization» more  GECCO 2010»
13 years 10 months ago
Efficiently evolving programs through the search for novelty
A significant challenge in genetic programming is premature convergence to local optima, which often prevents evolution from solving problems. This paper introduces to genetic pro...
Joel Lehman, Kenneth O. Stanley
AGENTS
2001
Springer
13 years 12 months ago
Using background knowledge to speed reinforcement learning in physical agents
This paper describes Icarus, an agent architecture that embeds a hierarchical reinforcement learning algorithm within a language for specifying agent behavior. An Icarus program e...
Daniel G. Shapiro, Pat Langley, Ross D. Shachter