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» Learning a Generative Model for Structural Representations
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AAAI
1992
13 years 10 months ago
Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning
It is often useful for a robot to construct a spatial representation of its environment from experiments and observations, in other words, to learn a map of its environment by exp...
Thomas Dean, Dana Angluin, Kenneth Basye, Sean P. ...
EGPGV
2011
Springer
366views Visualization» more  EGPGV 2011»
13 years 12 days ago
GPU Algorithms for Diamond-based Multiresolution Terrain Processing
We present parallel algorithms for processing, extracting and rendering adaptively sampled regular terrain datasets represented as a multiresolution model defined by a super-squa...
M. Adil Yalçin, Kenneth Weiss, Leila De Flo...
FLAIRS
2004
13 years 10 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
AAAI
2012
11 years 11 months ago
Manifold Warping: Manifold Alignment over Time
Knowledge transfer is computationally challenging, due in part to the curse of dimensionality, compounded by source and target domains expressed using different features (e.g., do...
Hoa Trong Vu, Clifton Carey, Sridhar Mahadevan
GIS
1992
ACM
14 years 29 days ago
Machine Induction of Geospatial Knowledge
Machine learning techniques such as tree induction have become accepted tools for developing generalisations of large data sets, typically for use with production rule systems in p...
Peter A. Whigham, Robert I. McKay, J. R. Davis