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Results 11 - 20
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109
A generalized path integral control approach to reinforcement learning
- The Journal of Machine Learning Research
, 2010
"... With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical techniques from optimal control and dynamic programming with modern learning techniques from statistical estimation theory. ..."
Abstract
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Cited by 70 (14 self)
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. In this vein, this paper suggests to use the framework of stochastic optimal control with path integrals to derive a novel approach to RL with parameterized policies. While solidly grounded in value function estimation and optimal control based on the stochastic Hamilton-Jacobi-Bellman (HJB) equations, policy
Integral projection models for species with complex demography.
- American Naturalist
, 2006
"... abstract: Matrix projection models occupy a central role in population and conservation biology. Matrix models divide a population into discrete classes, even if the structuring trait exhibits continuous variation (e.g., body size). The integral projection model (IPM) avoids discrete classes and po ..."
Abstract
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Cited by 42 (6 self)
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and potential artifacts from arbitrary class divisions, facilitates parsimonious modeling based on smooth relationships between individual state and demographic performance, and can be implemented with standard matrix software. Here, we extend the IPM to species with complex demographic attributes, including
Wavelet approximations to Jacobians and the inversion of complicated maps
"... Principal orthogonal decomposition can be used to solve two related problems: distinguishing elements from a collection by making d measurements, and inverting a complicated map from a p-parameter configuration space to a d-dimensional measurement space. In the case where d is more than 1000 or so ..."
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Cited by 1 (0 self)
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or so, the classical O(d 3 ) singular value decomposition algorithm becomes very costly, but it can be replaced with an approximate best-basis method that has complexity O(d 2 log d). This can be used to compute an approximate Jacobian for a complicated map from R p ! R d in the case p ø d. 1
Detection of indeterminacies in corrected ECG signals using parameterized multidimensional independent component analysis
- Computational and Mathematical Methods in Medicine
, 2009
"... Independent component analysis (ICA) is a new technique suitable for separating independent components from electrocardiogram (ECG) complex signals. The basic idea of using multidimensional independent component analysis (MICA) is to find stable higher dimensional source signal subspaces and to deco ..."
Abstract
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Cited by 1 (0 self)
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subspaces. The extracted independent components are further cleaned by statistical measures. In this study, it is also observed that the value of kurtosis coefficients for the independent components, which represents the noise component, can be further reduced using parameterized multidimensional ICA (PMICA
RELIEF MAPPING ON CUBIC CELL COMPLEXES
"... Abstract: In this paper we present an algorithm for parameterizing arbitrary surfaces onto a quadrilateral domain defined by a collection of cubic cells. The parameterization inside each cell is implicit and thus requires storing no texture coordinates. Based upon this parameterization, we propose a ..."
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representation has similar uses than geometry images, but it requires storing a single distance value per texel instead of full vertex coordinates. When combined with color and normal maps, our representation can be used to render an approximation of the model through an output-sensitive relief mapping algorithm
Learning of Class Membership Values by Ellipsoidal Decision Regions
"... Abstract—A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dime ..."
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Abstract—A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n
Learning of Class Membership Values by Ellipsoidal Decision Regions
"... Abstract—A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dime ..."
Abstract
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Abstract—A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n
Learning of Class Membership Values by Ellipsoidal Decision Regions
"... Abstract—A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dime ..."
Abstract
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Abstract—A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n
Multiresolution Representations for Surfaces Meshes
- In Proceedings of the SCCG
, 1997
"... We describe a method for constructing multi resolution models (MRM) of complex triangle meshes and show how these representations can be used to create view dependent adaptive approximations of triangle meshes on the fly with guaranteed approximation error. We use a modified one-sided Hausdorff d ..."
Abstract
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Cited by 29 (7 self)
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We describe a method for constructing multi resolution models (MRM) of complex triangle meshes and show how these representations can be used to create view dependent adaptive approximations of triangle meshes on the fly with guaranteed approximation error. We use a modified one-sided Hausdorff
Results 11 - 20
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109
