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Showing 1 to 20 of 98 for “"Function approximation"”.
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Training hierarchical networks for function approximation
In this work we investigate function approximation using Hierarchical Networks. We start of by investigating the theory proposed by Poggio et al [2] that Deep Learning Convolutional Neural Networks (DCN) can be equivalent to hierarchical kernel machines with the Radial Basis Functions (RBF).We …
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Sparse Value Function Approximation for Reinforcement Learning
… reinforcement learning (RL) algorithms is the approximation of the value function. The design and selection of features for approximation in RL is crucial, and an ongoing area of research. One approach to the problem of feature selection is to apply sparsity-inducing techniques in learning the …
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Why deep neural networks for function approximation
… show that, for a large class of piecewise smooth functions, the number of neurons needed by a shallow network to approximate a function is exponentially larger than the corresponding number of neurons needed by a deep network for a given degree of function approximation. First, we consider …
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Rational Function Approximation of Polynomials With Equiripple Error
Made available in DSpace on 2014-12-04T21:03:35Z (GMT). No. of bitstreams: 1 6305104.pdf: 2610002 bytes, checksum: 1ef0dfa34041ab96c8f80296994a627c (MD5) Previous issue date: 1963
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Value function approximation architectures for neuro-dynamic programming
… only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen? In this dissertation, we first propose an approach using the solutions to associated fluid and diffusion approximations. In order to evaluate this …
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Learning and value function approximation in complex decision processes
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
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Fast Clustering Using a Grid-Based Underlying Density Function Approximation
… use a concept called the “underlying density function”, which is a conceptual higher-dimension function that describes the possible results from the continuous data set that our input data is just a discrete sample of. The algorithm proposed in this paper seeks to use this concept by creating …
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Black Box Modeling of Passive Systems by Rational Function Approximation
… and accuracy compared to existing rational function approximation methods.
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Reinforcement learning under general function approximation and novel interaction settings
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms
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In-situ wafer uniformity estimation using principal component analysis and function approximation methods
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination
… set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A …
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Multiple machine maintenance : applying a separable value function approximation to a variation of the multiarmed bandit
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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Modeling correlations in clinical trial outcomes using machine learning
… sequence prediction, and a new approach using function approximation via random forest. This function approximation approach to estimating covariance is implemented and tested on historical clinical trial data.
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Real-time maneuvering decisions for autonomous air combat
… presents a method for formulating and solving a function approximation dynamic program to provide maneuvering decisions for autonomous one-on-one air combat. Value iteration techniques are used to compute a function approximation representing the solution to the dynamic program. The function …
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Efficient reduced-basis approximation of scalar nonlinear time-dependent convection-diffusion problems, and extension to compressible flow problems
… is used for the construction of reduced-basis approximation for the field variables. In the presence of highly nonlinear terms, conventional reduced-basis would be inefficient and no longer superior to classical numerical approaches using advanced iterative techniques. To recover the …
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Parameterizing transport maps for ensemble data assimilation
… examining the map parameterization for this function approximation problem. Using these ingredients, we introduce and discuss an algorithm that uses transport to perform online inference of the static parameters of an SSM, and relate this algorithm to prior methods. Finally, we tie the …
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Quasi-Newton and Multigrid Methods for Semiconductor Device Simulation
A finite difference approximation to the semiconductor device equations using the Bernoulli function approximation to the exponential function is described, and the robustness of this approximation is demonstrated. Sheikh's convergence analysis of Gummel's method and quasi-Newton methods is …
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Probabilistic characterization and synthesis of complex driven systems
… local models, is presented as a framework for function approximation and for the prediction and characterization of nonlinear time series. The general model architecture and estimation algorithm are presented and extended to system characterization tools such as estimator uncertainty, predictor …
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Application of cascade-correlation neural networks to nonlinear system identification
… has been shown to be capable of universal function approximation which makes it applicable to a much wider range of problems than other nonlinear identification techniques. While these neural networks show great potential, they still suffer several drawbacks, such as slow convergence toward …
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Characterizing complex time-series from the scaling of prediction error
… the global modeling technique of radial basis function approximation to build models from a state-space reconstruction of a time series that otherwise appears complicated or random (i.e. aperiodic, irregular). Prediction error as a function of prediction horizon is obtained from the model using …
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