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» Learning to Control in Operational Space
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AAAI
2012
13 years 6 months ago
Kernel-Based Reinforcement Learning on Representative States
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
Branislav Kveton, Georgios Theocharous
INFOCOM
2007
IEEE
15 years 10 months ago
Mutual Anonymous Communications: A New Covert Channel Based on Splitting Tree MAC
—Known covert channel based on splitting algorithms in Medium Access Control (MAC) protocols requires the receiver’s knowledge of the sender’s identity. In this paper we pres...
Zhenghong Wang, Jing Deng, Ruby B. Lee
HYBRID
2000
Springer
15 years 7 months ago
Towards Procedures for Systematically Deriving Hybrid Models of Complex Systems
Abstract. In many cases, complex system behaviors are naturally modeled as nonlinear differential equations. However, these equations are often hard to analyze because of "sti...
Pieter J. Mosterman, Gautam Biswas
ICCV
2009
IEEE
16 years 9 months ago
Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds
We present a manifold learning approach to dimensionality reduction that explicitly models the manifold as a mapping from low to high dimensional space. The manifold is represen...
Samuel Gerber, Tolga Tasdizen, Ross Whitaker

Book
695views
16 years 11 months ago
The Scheme Programming Language
"Scheme is a general-purpose computer programming language. It is a high-level language, supporting operations on structured data such as strings, lists, and vectors, as well ...
R. Kent Dybvig