Search - kernel

Search - kernel - List

[Algorithm] K_smooth DL : 0: The subroutines glkern.f and lokern.f use an efficient and fast algorithm for automatically adaptive nonparametric regression estimation with a kernel method. Roughly speaking, the method performs a local averaging of the observations when estimating the regression function. Analogously, one can estimate derivatives of small order of the regression function.
Date : 2026-01-10 Size : 190kb User : zhanglifang
[Algorithm] BPrepresentation DL : 0: BP学习算法应用——函数表达源代码实现了BPN的设计，使得通过训练后的BPN实现了函数表达，即BPN的输出与输入反映了特定的函数映射关系。代码中的具体应用实例为傅立叶核函数，应用BP学习算法拟合出傅立叶核函数，速度快，精度高。将源文件F_core_discription.m文件放入matlab的work文件夹中直接运行即可。-BP learning algorithm application- expression of the source code function BPN realize a design, through the training of BPN realize the function of expression, that is, the output and input BPN reflect a specific function mapping relations. Specific application code examples for Fourier kernel function, the application of BP learning algorithm for fitting a Fourier kernel function, high speed and accuracy. The source document file F_core_discription.m Add matlab folder of work can be directly run.
Date : 2026-01-10 Size : 2kb User : Michael_M
[Algorithm] lwpr DL : 0: 局部线性回归方法及其稳健形式已经被看作一种有效的非参数光滑方法.与流行的核回归方法相比,它有诸多优点,诸如:较高的渐近效率和较强的适应设计能力.另外,局部线性回归能适应几乎所有的回归设计情形却不需要任何边界修正。-Local linear regression methods and their solid form has been seen as an effective non-parametric smoothing method. Contrary to popular kernel regression methods, it has many advantages, such as: higher efficiency and stronger asymptotic adaptation design capacity. In addition, the local linear regression to adjust to the return of the design of almost all cases does not require any boundary amendment.
Date : 2026-01-10 Size : 1.28mb User : wanghuaqiu
[Algorithm] poly_svm DL : 1: 核函数是利用支持向量机解决不可分问题时引入的一种非线性变换的手段。基本思想是通过非线性变换，使样本变换之后的特征空间中变得线性可分。然后利用线性可分时构造最优超平面的方法，在特征空间中实现最优超平面的求解。-Kernel function is the use of support vector machine to resolve the issue can not be separated from the introduction of a nonlinear transform means. Basic idea is to adoption of non-linear transform, so that after changing the characteristics of the sample space become linearly separable. And the use of linear time-structure optimal hyperplane method of implementation in the feature space for solving the optimal hyperplane.
Date : 2026-01-10 Size : 4kb User : 王旭
[Algorithm] 45095smoothing DL : 1: 这个帖子中我想讨论的是移动窗口多项式最小二乘拟和平滑方法，粗糙惩罚方法，以及kernel平滑方法。-Posts in this discussion I think are moving window least squares polynomial fitting smoothing method, crude methods of punishment, as well as the kernel smoothing method.
Date : 2026-01-10 Size : 47kb User : linyuan
[Algorithm] kernel DL : 0: 用于实现支持向量机的核函数，常用的四种，多项式，线性，高斯-Used to implement SVM kernel function, four common
Date : 2026-01-10 Size : 1kb User : 程泽
[Algorithm] aok4win DL : 1: 自适应最优核函数计算时频分布的程序，用C++编写，可方便的实现aok时频分布的计算-Adaptive optimal kernel time-frequency distribution calculation program, written using C++, can facilitate the realization of the calculation of time-frequency distribution aok
Date : 2026-01-10 Size : 116kb User : watermelon
[Algorithm] randsin_clac DL : 0: Mathematica Kernel text input file "randsin_calc.txt". Place file in directory with Mathematica Kernel executable". If a probability level other than 99.865 percent is chosen for use, this value can be inserted into line 6.
Date : 2026-01-10 Size : 1kb User : ali
[Algorithm] Fortran77 DL : 0: 高震荡积分程序，可以计算以cos函数为积分核的高震荡积分-High shock points program that can calculate the cos function as integral kernel of the high-shock points
Date : 2026-01-10 Size : 368kb User : quanyamin
[Algorithm] Kernel-Adaptive-Filtering DL : 0: This book presents a comprehensive and unifying introduction to kernel adaptive ﬁ ltering. Adaptive signal processing theory has been built on three pillars: the linear model, the mean square cost, and the adaptive least - square learning algorithm. When nonlinear models are required, the simplicity of linear adaptive ﬁ lters evaporates and a designer has to deal with function approximation, neural networks, local minima, regularization, and so on. Is this the only way to go beyond the linear solution? Perhaps there is an alternative, which is the focus of this book. The basic concept is to perform adaptive ﬁ ltering in a linear space that is related nonlinearly to the original input space. If this is possible, then all three pillars and our intuition about linear models can still be of use, and we end up implementing nonlinear ﬁ lters in the input space. - This book presents a comprehensive and unifying introduction to kernel adaptive ﬁ ltering. Adaptive signal processing theory has been built on three pillars: the linear model, the mean square cost, and the adaptive least - square learning algorithm. When nonlinear models are required, the simplicity of linear adaptive ﬁ lters evaporates and a designer has to deal with function approximation, neural networks, local minima, regularization, and so on. Is this the only way to go beyond the linear solution? Perhaps there is an alternative, which is the focus of this book. The basic concept is to perform adaptive ﬁ ltering in a linear space that is related nonlinearly to the original input space. If this is possible, then all three pillars and our intuition about linear models can still be of use, and we end up implementing nonlinear ﬁ lters in the input space.
Date : 2026-01-10 Size : 1.38mb User : johnny
[Algorithm] EMTDC-Users-Guide DL : 0: PSCAD软件内核EMTDC的fortran书籍-PSCAD EMTDC software kernel of fortran books
Date : 2026-01-10 Size : 1.44mb User : 关于
[Algorithm] KPCA DL : 0: KPCA是一种基于核的主要成分分析，是一种由线性到非线性之间的桥梁。通过非线性函数把输入空间映射到高维空间，在特征空间中间型数据处理，引入核函数，把非线性变换后的特征空间内积运算转换为原始空间的核函数计算。基本思想是通过某种隐士方法将输入空间映射到某个高维空间（特征空间），并在特征空间实现PCA。对该算法进行了详细的说明-KPCA is a kernel-based principal components analysis, is a bridge between the linear to nonlinear. Nonlinear function to map the input space into a high dimensional space, in the middle of the feature space, data processing, the introduction of kernel function, product operation in the non-linear transformation of feature space for the kernel function of the original space calculation. The basic idea is that the input space by some kind of hermit method is mapped to a higher dimensional space (feature space), and the PCA in the feature space. The algorithm is a detailed description of
Date : 2026-01-10 Size : 1kb User : 张玉
[Algorithm] SVC-and-SVR DL : 1: 基于SVM数据分类及回归分析，并采用不同的核函数如RBF,sigmoid,polynomial等-the data classification and regression analysis based on SVM, by using different kinds of kernel functions, for examples, RBF,sigmoid and ploynomial and so on
Date : 2026-01-10 Size : 68kb User : candies
[Algorithm] kifmm3d.tar DL : 0: 3D Kernel independent fast multipole method. 此算法为上世纪十大算法之一，这是对于矩阵计算的O(N)的快速算法。-3D Kernel independent fast multipole method. Algorithm is one of the top ten algorithms of the last century, this is a fast algorithm for matrix calculation of O (N).
Date : 2026-01-10 Size : 37kb User : Zero
[Algorithm] pcaaKPCA DL : 0: 主分量分析和核主分量分析的原理简介，主分量分析（PCA）用于对信号进行特征提取和降维-Introduction of the principle of the principal component analysis and kernel principal component analysis, principal component analysis (PCA) for feature extraction and dimensionality reduction of signal
Date : 2026-01-10 Size : 116kb User : WL
[Algorithm] Kernel-Recursive-Least-Squares DL : 0: 递归核最小二乘算法，来至MIT大学的wingate教授，含6个源码，有实例！-dict_init.m- Part of the dictionary implementation used by KRLS algorithm. Can stand alone. dict.m- Part of the dictionary implementation used by the KRLS algorithm. Can stand alone. krls_init.m- Kernel recursive least squares initializer. krls.m- Main KRLS function. Repeatedly called with new data points. krls_query.m- Query the resulting estimator. krls_example.m- An example script showing the different parts.
Date : 2026-01-10 Size : 4kb User : jiang
[Algorithm] kernelICA-jmlr DL : 0: We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical properties as measures of statistical dependence. On the other hand, building on recent developments in kernel methods, we show that these criteria and their derivatives can be computed e±ciently. Minimizing these criteria leads to °exible and robust algorithms for ICA. We illustrate with simulations involving a wide variety of source distributions, showing that our algorithms outperform many of the presently known algorithms.
Date : 2026-01-10 Size : 374kb User : msreddy
[Algorithm] Intel-Math-Kernel-Library DL : 0: Intel Math Kernel Library 函数库用户参考手册，很全的手册，共3316页-Intel® Math Kernel Library11.0 Reference Manual
Date : 2026-01-10 Size : 12.83mb User : seabirdys
[Algorithm] feature_map.tar DL : 0: feature map for histogram intersection, map the data from hist intersection kernel to linear kernel-feature map for histogram intersection
Date : 2026-01-10 Size : 1kb User : bryant
[Algorithm] MKLGL DL : 0: generalized multiple kernel learning algorithm
Date : 2026-01-10 Size : 210kb User : AS

« 12 »