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» Parameterized Complexity and Approximation Algorithms
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ICMLA
2008
15 years 1 months ago
Basis Function Construction in Reinforcement Learning Using Cascade-Correlation Learning Architecture
In reinforcement learning, it is a common practice to map the state(-action) space to a different one using basis functions. This transformation aims to represent the input data i...
Sertan Girgin, Philippe Preux
NIPS
2008
15 years 1 months ago
Multi-Agent Filtering with Infinitely Nested Beliefs
In partially observable worlds with many agents, nested beliefs are formed when agents simultaneously reason about the unknown state of the world and the beliefs of the other agen...
Luke S. Zettlemoyer, Brian Milch, Leslie Pack Kael...
ICASSP
2011
IEEE
14 years 3 months ago
Error-entropy based channel state estimation of spatially correlated MIMO-OFDM
—This paper deals with optimized training sequences to estimate multiple-input multiple-output orthogonal frequencydivision multiplexing (MIMO-OFDM) channel states in the presenc...
Hoang Duong Tuan, Ha Hoang Kha, Ha H. Nguyen
ASUNAM
2011
IEEE
13 years 12 months ago
Probabilistic Subgraph Matching on Huge Social Networks
—Users querying massive social networks or RDF databases are often not 100% certain about what they are looking for due to the complexity of the query or heterogeneity of the dat...
Matthias Bröcheler, Andrea Pugliese, V. S. Su...
ECCV
2004
Springer
16 years 1 months ago
Multiple View Feature Descriptors from Image Sequences via Kernel Principal Component Analysis
Abstract. We present a method for learning feature descriptors using multiple images, motivated by the problems of mobile robot navigation and localization. The technique uses the ...
Jason Meltzer, Ming-Hsuan Yang, Rakesh Gupta, Stef...