"Radial basis function"의 두 판 사이의 차이

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== 노트 ==
 
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===위키데이터===
 
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* ID :  [https://www.wikidata.org/wiki/Q1588488 Q1588488]
 
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===말뭉치===
 
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# A Radial basis function is a function whose value depends only on the distance from the origin.<ref name="ref_5f1e1abb">[https://deepai.org/machine-learning-glossary-and-terms/radial-basis-function Radial Basis Functions]</ref>
 
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# A Radial basis function works by defining itself by the distance from its origin or center.<ref name="ref_5f1e1abb" />
 
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# The Gaussian variation of the Radial Basis Function, often applied in Radial Basis Function Networks, is a popular alternative.<ref name="ref_5f1e1abb" />
 
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# Error estimates for matrix-valued radial basis function interpolation Journal of Approximation Theory 137: 234-249.<ref name="ref_f77dadaa">[http://www.scholarpedia.org/article/Radial_basis_function Radial basis function]</ref>
 
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# Sobolev bounds on functions with scattered zeros, with applications to radial basis function surface fitting Mathematics of Computation 74: 643-763.<ref name="ref_f77dadaa" />
 
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# Local error estimates for radial basis function interpolation of scattered data IMA Journal of Numerical Analysis 13: 13-27.<ref name="ref_f77dadaa" />
 
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# To avoid this problem, the radial basis function interpolation approach can be applied.<ref name="ref_cf3b7dfe">[https://pubs.acs.org/doi/10.1021/ci400704f A New Approach to Radial Basis Function Approximation and Its Application to QSAR]</ref>
 
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# For example, suppose the radial basis function is simply the distance from each location, so it forms an inverted cone over each location.<ref name="ref_4fdfa48d">[https://pro.arcgis.com/en/pro-app/latest/help/analysis/geostatistical-analyst/how-radial-basis-functions-work.htm How radial basis functions work—ArcGIS Pro]</ref>
 
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# If you take a cross section of the x,z plane for y = 5, you will see a slice of each radial basis function.<ref name="ref_4fdfa48d" />
 
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# In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors.<ref name="ref_e8437dba">[https://www.mdpi.com/1424-8220/18/9/3091 An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas]</ref>
 
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# The radial basis function, based on the radius, r, given by the norm (default is Euclidean distance); the default is ‘multiquadric’: 'multiquadric' : sqrt (( r / self .<ref name="ref_e926bf55">[https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.Rbf.html scipy.interpolate.Rbf — SciPy v1.5.4 Reference Guide]</ref>
 
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# Radial basis function methods are modern ways to approximate multivariate functions, especially in the absence of grid data.<ref name="ref_334a82b1">[https://www.cambridge.org/core/journals/acta-numerica/article/radial-basis-functions/3FD3A8BBC9B020FA349305142D0EB367 Radial basis functions]</ref>
 
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# AR-RBFN utilizes radial basis function networks (RBFN) initially proposed to perform accurate interpolation of data points in a multidimensional space21.<ref name="ref_b9b14e7e">[https://www.nature.com/articles/s41598-020-60606-1 Attractor Ranked Radial Basis Function Network: A Nonparametric Forecasting Approach for Chaotic Dynamic Systems]</ref>
 
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# Figure 2 Attractor ranked radial basis function network.<ref name="ref_b9b14e7e" />
 
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# Learning radial basis function network requires the determination of RBF weights and centers.<ref name="ref_b9b14e7e" />
 
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# (10), the type of the radial basis function \({\psi }_{\rho }({M}_{l}^{g})\)is taken as Gaussian kernels whose inputs are \(E\)-dimensional vectors of a combination of variables and time-lags.<ref name="ref_b9b14e7e" />
 
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# To get a better characterization of freeform surface, Gaussian radial basis function (RBF) model was first proposed and applied in the design of the head-worn display (HWD) systems by Cakmakci et al.<ref name="ref_d9606bce">[https://www.osapublishing.org/abstract.cfm?uri=oe-26-11-14010 Model of radial basis functions based on surface slope for optical freeform surfaces]</ref>
 
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# (166) To find a compactly supported radial basis function, the coefficients for both the numerator and the denominator must be found to ensure the compactness of the support.<ref name="ref_d766905f">[https://onlinelibrary.wiley.com/doi/abs/10.1002/eng2.12028 Two new classes of compactly supported radial basis functions for approximation of discrete and continuous data]</ref>
 
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# RBF, radial basis function As can be observed, the inverse functions perform as well as both Wendland or Wu functions, and the rational functions also perform similarly.<ref name="ref_d766905f" />
 
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# The toolbox is called the Matlab Radial Basis Function Toolbox (MRBFT).<ref name="ref_06341f38">[https://openresearchsoftware.metajnl.com/articles/10.5334/jors.131/ The Matlab Radial Basis Function Toolbox]</ref>
 
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# The result of a radial basis function applied to two vectors is a single value where smaller values indicate the two vectors are farther apart.<ref name="ref_575b2b83">[https://visualstudiomagazine.com/articles/2020/03/12/create-radial-basis-function.aspx How to Create a Radial Basis Function Network Using C# -- Visual Studio Magazine]</ref>
 
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# {http://proceedings.mlr.press/v51/que16.html}, abstract = {Radial Basis Function (RBF) networks are a classical family of algorithms for supervised learning.<ref name="ref_a4a70006">[http://proceedings.mlr.press/v51/que16.html Back to the Future: Radial Basis Function Networks Revisited]</ref>
 
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# %V 51 %W PMLR %X Radial Basis Function (RBF) networks are a classical family of algorithms for supervised learning.<ref name="ref_a4a70006" />
 
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# The task mentioned above — magically separating points with one line — is known as the radial basis function kernel, with applications in the powerful Support Vector Machine (SVM) algorithm.<ref name="ref_b05c7ee2">[https://medium.com/analytics-vidhya/radial-basis-functions-rbf-kernels-rbf-networks-explained-simply-35b246c4b76c Radial Basis Functions, RBF Kernels, & RBF Networks Explained Simply]</ref>
 
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# We present a radial basis function solver for convolutional neural networks that can be directly applied to both distance metric learning and classification problems.<ref name="ref_73850ceb">[https://openreview.net/forum?id=SkFEGHx0Z Nearest Neighbour Radial Basis Function Solvers for Deep Neural...]</ref>
 
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# Our method treats all training features from a deep neural network as radial basis function centres and computes loss by summing the influence of a feature's nearby centres in the embedding space.<ref name="ref_73850ceb" />
 
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# Having a radial basis function centred on each training feature is made scalable by treating it as an approximate nearest neighbour search problem.<ref name="ref_73850ceb" />
 
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# We show that our radial basis function solver outperforms state-of-the-art embedding approaches on the Stanford Cars196 and CUB-200-2011 datasets.<ref name="ref_73850ceb" />
 
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# The radial basis function (RBF) surrogate model represents the interpolating function as a linear combination of basis functions, one for each training point.<ref name="ref_f9be6ea7">[https://smt.readthedocs.io/en/latest/_src_docs/surrogate_models/rbf.html Radial basis functions — SMT 0.7.1 documentation]</ref>
 
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# A radial basis function (RBF) network is a software system that can classify data and make predictions.<ref name="ref_9b003e8e">[https://docs.microsoft.com/en-us/archive/msdn-magazine/2013/december/test-run-radial-basis-function-network-training Test Run - Radial Basis Function Network Training]</ref>
 
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# This article assumes you have advanced programming skills with C# and a basic familiarity with the radial basis function network input-process-output mechanism.<ref name="ref_9b003e8e" />
 
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# Using the distance value by itself is called a linear radial basis function or linear RBF.<ref name="ref_0c7a4eca">[https://www.chadvernon.com/blog/rbf/ Regularized Linear Regression with Radial Basis Functions]</ref>
 
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== 노트 ==
 
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==메타데이터==
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===위키데이터===
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* ID :  [https://www.wikidata.org/wiki/Q1588488 Q1588488]
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===Spacy 패턴 목록===
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* [{'LOWER': 'radial'}, {'LOWER': 'basis'}, {'LEMMA': 'function'}]

2021년 2월 16일 (화) 23:39 기준 최신판

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말뭉치

  1. A radial basis function (RBF) network has been suggested as one of the most suitable multilayer network algorithms, quick to train and efficient to map any nonlinear input–output relationships.[1]
  2. → output is a real value → each neuron have Radial Basis function → centred on the point of the same dimension.[2]
  3. The radial basis function for a neuron has a center and a radius (also called a spread).[3]
  4. Each neuron consists of a radial basis function centered on a point with as many dimensions as there are predictor variables.[3]
  5. N2 - Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains.[4]
  6. AB - Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains.[4]
  7. Optimized K-means Segmentation and Radial Basis Function Neural Networks,” International Journal of Information and Communication Technology Research (IJICT), vol.[5]
  8. In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation functions.[6]
  9. Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control.[6]
  10. Functions that depend only on the distance from a center vector are radially symmetric about that vector, hence the name radial basis function.[6]
  11. A Radial basis function is a function whose value depends only on the distance from the origin.[7]
  12. A Radial basis function works by defining itself by the distance from its origin or center.[7]
  13. The Gaussian variation of the Radial Basis Function, often applied in Radial Basis Function Networks, is a popular alternative.[7]
  14. Error estimates for matrix-valued radial basis function interpolation Journal of Approximation Theory 137: 234-249.[8]
  15. Sobolev bounds on functions with scattered zeros, with applications to radial basis function surface fitting Mathematics of Computation 74: 643-763.[8]
  16. Local error estimates for radial basis function interpolation of scattered data IMA Journal of Numerical Analysis 13: 13-27.[8]
  17. Radial Basis Function Networks (RBF nets) are used for exactly this scenario: regression or function approximation.[9]
  18. They are similar to 2-layer networks, but we replace the activation function with a radial basis function, specifically a Gaussian radial basis function.[9]
  19. Each hidden neuron has a radial basis function which is a center symmetric nonlinear function with local distribution.[10]
  20. The radial basis function consists of a center position and a width parameter.[10]
  21. is Euclidean norm usually taking 2-norm. is the radial basis function.[10]
  22. Based on the EDIW-PSO algorithm, we optimize the centers, widths, and connection weights of radial basis function (RBF) neural network.[10]
  23. This paper proposed a radial basis function (RBF) neural network method to forecast the wind power generation of WECS.[11]
  24. Three parameterize RBFNs model with the centers and spreads of each radial basis function, and the connection weights to solve the mobile robot path traveling and routing problems.[11]
  25. A major kind of neural network, i.e. radial basis function neural network (RBFNN), is used to model the fault diagnosis structure.[11]
  26. A new algorithm for training radial basis function neural network (RBFNN) is presented in this paper.[11]
  27. The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN).[12]
  28. We present a radial basis function solver for convolutional neural networks that can be directly applied to both distance metric learning and classification problems.[13]
  29. Our method treats all training features from a deep neural network as radial basis function centres and computes loss by summing the influence of a feature's nearby centres in the embedding space.[13]
  30. Having a radial basis function centred on each training feature is made scalable by treating it as an approximate nearest neighbour search problem.[13]
  31. We show that our radial basis function solver outperforms state-of-the-art embedding approaches on the Stanford Cars196 and CUB-200-2011 datasets.[13]
  32. An important feature of radial basis function neural networks is the existence of a fast, linear learning algorithm in a network capable of representing complex nonlinear mappings.[14]

소스

  1. ↑ Radial Basis Function Networks - an overview
  2. ↑ [ Archived Post Radial Basis Function Artificial Neural Networks]
  3. ↑ 3.0 3.1 Software Analysis Files and Solutions
  4. ↑ 4.0 4.1 Radial basis function neural networks: A topical state-of-the-art survey
  5. ↑ Radial Basis Function Neural Networks (with parameter selection using K-means)
  6. ↑ 6.0 6.1 6.2 Radial basis function network
  7. ↑ 7.0 7.1 7.2 Radial Basis Functions
  8. ↑ 8.0 8.1 8.2 Radial basis function
  9. ↑ 9.0 9.1 Using Neural Networks for Regression: Radial Basis Function Networks
  10. ↑ 10.0 10.1 10.2 10.3 Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction
  11. ↑ 11.0 11.1 11.2 11.3 Radial Basis Function Neural Network (RBFNN)
  12. ↑ Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments
  13. ↑ 13.0 13.1 13.2 13.3 Nearest Neighbour Radial Basis Function Solvers for Deep Neural...
  14. ↑ Improving the Generalization Properties of Radial Basis Function Neural Networks

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Spacy 패턴 목록

  • [{'LOWER': 'radial'}, {'LOWER': 'basis'}, {'LEMMA': 'function'}]
원본 주소 "https://wiki.mathnt.net/index.php?title=Radial_basis_function&oldid=51020"