ライフサイエンスコーパス: basis function

1 k analysis (multilayer perceptron and radial basis function).

2 covariance matrix based on a Gaussian radial basis function.

3 plines instead of a deep neural network as a basis function.

4 and spectral analysis with multiple sets of basis functions.

5 ctorization models using shape or trajectory basis functions.

6 characterization of the decay rate of these basis functions.

7 l approximations are implemented with 6-31G* basis functions.

8 sing the rich conceptual framework of neural basis functions.

9 obenius operator by means of a finite set of basis functions.

10 e intermediate representation that relies on basis functions.

11 rained and compressed with suitable harmonic basis functions.

12 escribed by linear combinations of arbitrary basis functions.

13 verning equations from a large dictionary of basis functions.

14 lowed by Kane (0.44 D +/- 0.75), Hill-radial basis function (0.47 D +/- 0.74), Holladay II (0.47 D +/

15 wed by Kane (56.7%, 51 eyes) and Hill-radial basis function (54.4%, 49 eyes).

16 The basis functions align with social interaction types, as

17 refraction between +0.046 D for Hill-Radial Basis Function and +0.097 D for Haigis.

18 (SSVS), support vector machines with radial basis function and epsilon regression (SVM-R(EPS)), supp

19 R(EPS)), support vector machines with radial basis function and nu regression (SVM-R(NU)), support ve

20 and support vector machines using the radial basis function and polynomial kernel function, we found

21 does not depend upon a particular choice of basis functions and is applicable across quantum computa

22 icient numerical algorithms for finding such basis functions and the reduced (or compressed) operator

23 terface elements, represented by the regular basis functions, and bounded independently of the interf

24 required scales linearly with the number of basis functions, and the number of gates required grows

25 d were evaluated: nonlinear optimization and basis function approach.

26 The calibrations developed with the Gaussian basis functions are compared to conventional calibration

27 The basis functions are determined by use of a numerical opt

28 These basis functions are related by analogy to optical filter

29 The use of basis functions as an intermediate is borrowed from the

30 ction of a highly compact set of Hamiltonian basis functions, based on molecular interaction potentia

31 optimal cutoff was derived by using a radial basis function-based support vector machine.

32 ng plasma input Logan graphic analysis and 2 basis functions-based a 2-tissue-compartment basis funct

33 ng plasma input Logan graphic analysis and 2 basis functions-based methods: a 2-tissue-compartment ba

34 local rotation turns each pi to a tangential basis function, changing bonding interactions to antibon

35 along with the l1 -minimization posed on the basis function coefficients the ResNet finally provides

36 ation and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in th

37 The ROP was modelled using Radial Basis Function, Decision Tree (DT), Least Square Vector

38 ing identified early and late timecourses as basis functions for decomposing responses into component

39 ctions (EOFs) of ISMR over India are used as basis functions for elucidating these relationships.

40 ett Universal II (BUII), Haigis, Hill-Radial Basis Function (Hill-RBFv3.0), Hoffer Q, Holladay I, Hol

41 The basis function implementation of SRTM demonstrated impro

42 e versions of Ichise, reference Logan, and 2 basis function implementations (receptor parametric mapp

43 Voxel-level analysis was performed using basis function implementations of SRTM, reference Logan,

44 rfusion images were calculated using several basis function implementations of the single-tissue-comp

45 ortex can be modeled to function as a set of basis functions in a lossy representation such as the we

46 ng chemical species by a linear expansion of basis functions in such a manner that the coupled reacti

47 ficient to use a limited number of spherical basis functions in the Fourier space, which increases th

48 ctivity) and a new set of graphical analysis basis functions, including a new definition of normalize

49 mixture of regression model (MRM) of radial basis functions, integrating information of neighboring

50 he highest MedAE were found with Hill-Radial Basis Function (IOLup1D) and Sanders-Retzlaff-Kraff/theo

51 uses a Support Vector Machine with a Radial Basis Function kernel (SVM(RBF)) as a base learner and e

52 support vector regression (SVR) with radial basis function kernel and random forest (p < 0.001 for e

53 f support vector machines utilizing a radial basis function kernel for predicting nuclear alpha-decay

54 t vector machine (SVM) model with the radial basis function kernel had the maximum accuracy (78%) in

55 t of 240 miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fol

56 ints demonstrates that the use of the radial basis function kernel yields predictive models with root

57 deletion (support vector machine with radial basis function kernel) with an area under the curve of 0

58 ion (LR), support vector machine with radial-basis function kernel, artificial neural network, random

59 a Gaussian Process regression with a radial basis function kernel.

60 To do so, we present complete sets of basis functions learned with slow subspace analysis (SSA

61 include support vector machines with radial basis function, linear, and polynomial kernels; artifici

62 Parametric Ki images were computed using a basis function method (BFM) implementation of the 2-tiss

63 g the various parametric methods tested, the basis function method provided parametric VT and K1 valu

64 g the various parametric methods tested, the basis function method provided parametric VT and K1 valu

65 were generated using Logan plot analysis, a basis function method, and spectral analysis.

66 basis functions-based a 2-tissue-compartment basis function model (BFM) and spectral analysis (SA).

67 ctions-based methods: a 2-tissue-compartment basis function model (BFM) and spectral analysis (SA).

68 atial correlations across the genome through basis function modeling as well as correlations between

69 w that our approach, attractor ranked radial basis function network (AR-RBFN) provides a better forec

70 IPL complex predicted by the Bayesian radial basis function network provides better diagnostic utilit

71 s of the metal ions were processed by radial basis function networks (RBFNs) and feed forward neural

72 Thus, basis function networks with multidimensional attractors

73 that a particular class of neural networks, basis function networks with multidimensional attractors

74 nterviews with IWS households, we use radial basis function networks, a type of artificial neural net

75 tivariate autoregressive models using radial basis function networks.

76 quare Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), Multi-Layer Perce

77 The package of Bayesian bio-basis function neural network can be obtained by request

78 compared to a soft sensor based on a radial basis function neural network reported in a previous stu

79 The results show that the Bayesian bio-basis function neural network with two Gaussian distribu

80 n application of our recently developed 'bio-basis function neural network' pattern recognition algor

81 near discriminant analysis (LDA), and radial basis function neural networks (RBFNN), are used to cate

82 ns, multilayer perceptrons, the original bio-basis function neural networks and support vector machin

83 Bayesian bio-basis function neural networks are investigated and the

84 This study employs Radial Basis Function Neural Networks as surrogate models train

85 study, to investigate the application of bio-basis function neural networks for the prediction of cas

86 As bio-basis function neural networks have proven to outperform

87 ge sites in O-linked glycoproteins using bio-basis function neural networks.

88 -specific ion channel models to serve as the basis functions of our C-fiber models.

89 different spatial scales), comparable to the basis functions of wavelet transforms.

90 The bio-basis function proposed by Thomson et al. is used to tra

91 An SVM classifier with a radial basis function provided classification accuracy from 95.

92 overlapping, individual-specific topographic basis functions, rather than as contiguous functional ar

93 he Olsen (4-factor), Haigis, and Hill-radial basis function (RBF) 1.0.

94 ing support vector machine (SVM) with radial basis function (RBF) kernel and leave-one-out method to

95 a support vector machine (SVM) with a radial basis function (RBF) kernel.

96 ts, generalized regression (GRNN) and radial basis function (RBF) neural networks were developed to p

97 Radial Basis Function (RBF) outperformed polynomial and linear

98 ion (SVR), K-nearest neighbors (KNN), Radial basis function (RBF), and Multilayer Perceptron (MLP) op

99 te adaptive regression spline (MARS), radial basis function (RBF), and multilayer perceptron (MLP) wi

100 , convolutional neural network (CNN), radial basis function (RBF), gated recurrent units (GRUs), and

101 approach of multilayer perceptron and radial basis functions (RBF) were developed based on the homoge

102 e and flexible reduced-order model-based the basis function (RMBF) that combines CE-QUAL-W2 (W2) and

103 bration approach is based on Gaussian radial basis function support vector classifier (RBF-SVC) that

104 Radial basis function support vector machine classifiers with c

105 te that the proposed framework can cope with basis functions that have nonlinear (unknown) parameteri

106 from -0.039 diopters (D) for the Hill-Radial Basis Function to -0.096 D for Haigis.

107 the favorable properties of our custom-made basis functions to both study their approximation capabi

108 orm yet larger grids and can also be used as basis functions to construct memory representations of s

109 are constructed through the use of Gaussian basis functions to extract relevant information from sin

110 The CDT uses radial basis function transforms with distances constrained to

111 ter fitting was employed to adjust a release basis function until the model output fitted recorded (2

112 do so in a compressed format resembling the basis functions used in spatial, visual and motor domain

113 ectiveness depend crucially on the choice of basis functions used to expand the solution of a PDE.

114 jecting the signal onto a set of oscillatory basis functions using a Discrete Fourier Transform.

115 a, which is a linear combination of the FPLS-basis functions, using simulated annealing algorithm.

116 n CPD (Coherent Point Drift) and RBF (Radial Basis Function) was proposed to achieve the rapid develo

117 ) We first highlight that Gabor-like sets of basis functions, which are similar to the receptive fiel

118 ctral analysis-derived V(T) with a set of 30 basis functions with exponents ranging from 0.0175 to 1.