Description: 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. Platform: |
Size: 194560 |
Author:zhanglifang |
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Description: 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. Platform: |
Size: 1346560 |
Author:wanghuaqiu |
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Description: 非参数统计学中非参数回归的简单应用核回归程序，应用范围广泛，不需要知道样本的分布就可以使用该方法。-Non-parametric statistical regression Nonparametric kernel regression of the simple application procedure, a wide range of applications, does not need to know the distribution of the samples you can use this method. Platform: |
Size: 2048 |
Author:林森 |
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Description: MATLAB程序，
半参数线性回归模型的最小二乘核估计
半参数线性回归模型的最小二乘正交序列估计。-MATLAB program, semi-parametric linear regression model of least squares kernel estimation Semiparametric least squares linear regression model orthogonal sequence estimation. Platform: |
Size: 1024 |
Author:wyh |
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Description: SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X --> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
-SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
Platform: |
Size: 117760 |
Author:jon |
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Description: this program compare the Locally Weighted Linear Regression with three diferrent kernel function (gaussian, logistic basis, and Reciprocal Multiquadric) also compare locally weighted by simple Linear Regression. Platform: |
Size: 9216 |
Author:maisam |
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Description: ：为了提高最小二乘支持向量回归机的性能，将Morlet小波核函数引入其中，形成了最小二乘小波支
持向量回归机模型。利用待优化的参数重构模型的目标函数和约束条件，并在此基础上通过遗传算法进行参数
选择，从而提高了该模型的泛化能力。将最小二乘小波支持向量回归机应用于导弹陀螺仪的漂移趋势预测，仿真
实验结果表明了该方法的有效性和可行性，因此可以为陀螺仪的故障预报、可靠性辅助决策提供依据。-To improve the ability of least square support vector regression algorithm，a least square wavelet
support vector regression model by introducing the Morlet wavelet kernel is presented．The object function and
constraint condition are reconstructed by the parameters to be optimized．On the base of it，the model parame—
ters are optimized through genetic algorithm．As a result，the model attains the better generalization ability．
The least square wavelet support vector regression model is used tO forecast the missile gyroscope’S drift tend—
ency．The simulation experiment results indicate the feasibility and validation of the algorithm．So it can provide
basis for the gyroscope’S fault prediction and reliability aid decision． Platform: |
Size: 285696 |
Author:11 |
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Description: In kernel ridge regression we have seen the final solution was not sparse in the variables ® .
We will now formulate a regression method that is sparse, i.e. it has the concept of support
vectors that determine the solution.
The thing to notice is that the sparseness arose from complementary slackness conditions
which in turn came from the fact that we had inequality constraints. In the SVM the penalty
that was paid for being on the wrong side of the support plane was given by C
P
i » k
i for
positive integers k, where » i is the orthogonal distance away from the support plane. Note
that the term jjwjj2 was there to penalize large w and hence to regularize the solution.
Importantly, there was no penalt Platform: |
Size: 51200 |
Author:bahman |
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Description: 基于结构张量的核回归非均匀插值算法及其在图像处理中的应用-Structure tensor based on non-uniform interpolation kernel regression algorithm and its application in image processing Platform: |
Size: 5646336 |
Author:张林杰 |
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Description: 基于核函数回归方法的图像去噪，图像平滑。对于图像领域的研究者有很大作用-Kernel regression method based on image denoising, image smoothing. Researchers in the field for the image plays a significant role Platform: |
Size: 234496 |
Author:郝人 |
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Description: 用核回归方法实现图像去噪是目前理论上最先进的图像去噪方法，这里提供的是图像去噪的matlab代码。-Kernel regression method with denoising is theoretically the most advanced image denoising method, here is the matlab code for image denoising. Platform: |
Size: 5120 |
Author:yanxiaoyun |
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Description: 主要讲述有关核回归的内容，非常经典，很具有参考价值，对大家了解这一方面或者做研究很有帮助，希望对大家有帮助-Focuses on kernel regression, very classic, very valuable reference for all of us to understand this aspect or helpful in doing research, we hope to help Platform: |
Size: 556032 |
Author:haowangli |
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Description: In this paper, we make contact with the field of nonparametric
statistics and present a development and generalization
of tools and results for use in image processing and reconstruction.
In particular, we adapt and expand kernel regression ideas
for use in image denoising, upscaling, interpolation, fusion, and
more. Furthermore, we establish key relationships with some popular
existing methods and show how several of these algorithms,
including the recently popularized bilateral filter, are special cases
of the proposed framework. The resulting algorithms and analyses
are amply illustrated with practical examples. Platform: |
Size: 9319424 |
Author:ionutmirel |
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Search - kernel regression