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 and spectral analysis with multiple sets of basis functions.

4 ctorization models using shape or trajectory basis functions.

5 characterization of the decay rate of these basis functions.

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

7 sing the rich conceptual framework of neural basis functions.

8 e intermediate representation that relies on basis functions.

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

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

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

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

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

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

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

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

17 These basis functions are related by analogy to optical filter

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

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

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

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

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

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

24 The basis function implementation of SRTM demonstrated impro

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

40 Thus, basis function networks with multidimensional attractors

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

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

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

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

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

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

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

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

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

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

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

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

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

54 Radial Basis Function (RBF) outperformed polynomial and linear

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

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

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

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

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

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