Description: KPCA与SVM共同用于人脸识别 SVM提高了分类效果 KPCA是一种借鉴SVM中核函数的一种较好的特征提取方法-KPCA and SVM for face recognition SVM together to improve the classification results from KPCA is a kernel function in SVM a better feature extraction method Platform: |
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Author:付赛男 |
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Description: kernel adatron, svm impelemtation using gradient ascent method, fast and accurate for solving SVM problem with two classes Platform: |
Size: 5120 |
Author:budi santosa |
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Description: 基于Gauss 核函数SVM分类机，使用二阶几何方法训练。-Gauss kernel function SVM classification based on machine, using the geometric method of second-order training. Platform: |
Size: 778240 |
Author:谌叶龙 |
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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: 核主成分分析方法，是主成分分析的一种改进算法，是一种非线性的特征提取方法。
-Kernel principal component analysis, is the principal component analysis of an improved algorithm, is a nonlinear feature extraction method. Platform: |
Size: 1024 |
Author:叶子 |
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Description: 在ORL或Yale标准人脸数据库上完成模式识别任务。用PCA与基于核的PCA（KPCA）方法完成人脸图像的重构与识别试验.
-Or Yale in the ORL face database, complete the standard pattern recognition tasks. With the PCA and kernel-based PCA (KPCA) method to complete the reconstruction of face image and recognition test. Platform: |
Size: 1024 |
Author:李海 |
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Description: 最小二乘隐空间支持向量机
王玲 薄列峰 刘芳 焦李成
!
在隐空间中采用最小二乘损失函数$提出了 最 小 二 乘 隐 空 间 支 持 向 量 机#0*&**52H 8
同 隐 空 间 支 持 向
量机#&**52H 一样$最小二乘隐空间支持向量机不需 要 核 函 数 满 足 正 定 条 件$从 而 扩 展 了 支 持 向 量 机 核 函 数 的
选择范围
8
由于采用了最小二乘损失函数$最小二乘隐空间支持向量机产生的优 化 问 题 为 无 约 束 凸 二 次 规 划$这 比
隐空间支持向量机产生的约束凸二次规划更易求解
8
仿真实验结果表明所提算法在 计 算 时 间 和 推 广 能 力 上 较 隐 空
间支持向量机存在一定的优势
-In the hidden space, using the least square loss function space $ proposed least squares support vector machine hidden# 0* &** 52H 8
Space with the implicit support to the
The amount of machine# &** 52H $ as an implicit least squares support vector machines do not need space to meet definite conditions $ kernel function extends SVM kernel function
Range of options
8
As a result of an implicit least squares loss function space $ least squares support vector machine optimization problem arising from the unconstrained convex quadratic programming for the $ than
Hidden space support vector machines produce more constrained convex quadratic programming solver
8
Simulation results show that the proposed method in computation time and the ability to promote a more implicit empty
Between the support vector machine there is a certain advantage Platform: |
Size: 161792 |
Author:lux |
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Description: 为了准确地对监控场景中的运动目标进行语义上的分类， 提出了一种基于聚类的核主成分分析梯度方向直方图和二叉决策树支持向量机的运动目标分类算法．利用背景减法提取运动目标前景区域， 并识别出潜在候选运动目标．利
用提出的基于聚类的核主成分分析的梯度直方图描述子提取候选运动目标的特征， 以较低维数的数据有效地描述运动目标的有效特征． 将提取的运动目标特征输入二叉决策树支持向量机， 实现多类目标的准确分类． 通过在不同视频序列上的实验验证， 提出的算法对运动目标进行较好地分类， 而且在运算速度方面较传统目标分类方法有了明显的提高． 实验结果证明了算法对运动目标分类具有较好的准确性 可靠性和鲁棒性．-For the purpose of semantically classifying moving objects accurately in a surveillance scene，a moving objects classification method based on the clustered kernel principal component analysis ( CKPCA) of the histogram
of oriented gradients ( HOG) and support vector machine ( SVM) was proposed． Firstly，the moving areas in the
foreground were extracted by means of the background subtraction method，and some of them were identified as potential candidates of moving objects． Secondly，the characteristics of the moving objects were obtained by the CKPCA- HOG descriptor，which could describe the moving objects＇ effective features at a lower data dimension． Finally，the data characteristics were fed into a binary SVM decision tree，and the final multi- class classification results were obtained accurately． After verifying different video sequences，the algorithm was able to classify moving targets very well． Compared with traditional classification methods，the proposed method makes obvious improv Platform: |
Size: 272384 |
Author:高峰 |
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Description: 讲了支持向量机关于分类的方法，利用不同的核函数进行分类。-Talking about the method of support vector machine classification, using different kernel function classification Platform: |
Size: 319488 |
Author:sungaoyan |
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Description: 针对基于GPS／GIS的浮动车数据特点，总结其中无效的数据类型，并给出数据有效处理的方法。以支持向量机原理、交通状态预测方法为基础，分析了常用支持向量回归机、核函数及模型参数的性能，以及各核函数及模型参数对支持向量机性能的影响及作用。针对路段平均速度预测中的小样本、非线性、高维回归等特点，将支持向量回归机方法引入基于浮动车数据的路段车辆速度预测，构建了路段平均速度短时预测模型。并以杭州市某路段的实际数据为例，详细阐述了支持向量回归机预测模型的具体建模和求解过程。运用LibSVM2．84软件包，进行预测模型的参数选择、样本训练以及预测求解，并通过预测结果的对比分析，验证了预测模型的可用性和有效性。-Characteristics of the GPS/GIS-based floating car data, summary of which types of invalid data, and gives the effective data processing method. Support vector machine, the traffic state prediction method based on analysis of commonly used support vector regression machines, nuclear function and performance of the model parameters, as well as the kernel function and model parameters on the performance of SVM and effect. Small sample, nonlinear, high dimensional regression for the prediction of average speed characteristics, the support vector regression machine to the introduction of prediction based on floating car data section of the speed of vehicles, build a short-term forecasting model of average speed. In Hangzhou section of the actual data, for example, elaborated on the specific modeling and solving process of the support vector machine for regression prediction model. Use LibSVM2.84 package forecast model parameter selection, sample training, and forecasting solution, and verif Platform: |
Size: 2207744 |
Author:tangshx |
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Description: 基于SVM的图像分类，由于支持向量机的分类能力极大地依赖于核参数的选取，因此，本文着重研究了核参数选择方法，并利用不同的颜色、纹理特征对图像进行分类。
-SVM-based image classification, the classification capability of SVM kernel parameters greatly depend on the selection, therefore, this paper focuses on the kernel parameter selection method, and use a different color, texture features for image classification. Platform: |
Size: 3573760 |
Author:ww1 |
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Description: 基于PSO训练SVM的人脸识别利用支持向量机在学习能力方面表现的良好性能,结合核主元分析特征提取方法,将将其应用于人脸识别中,该方法在实验中表现了良好的识别性能,为人脸识别领域提供了一条新的识别途径 已通过测试。
-Good performance, performance in the ability to learn the use of support vector machines based on PSO training SVM face recognition combined kernel principal component analysis feature extraction method will be applied to the face recognition, the method in experiments demonstrated good recognition performance identify a new pathway for face recognition has been tested. Platform: |
Size: 1098752 |
Author:wgh |
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Description: 提出了一种支持矢量机的汉语声调识别新方法。论文首先在基频和对数能量的基础上，建立了一个适合于支
持矢量机分类的等维声调特征。然后对支持矢量机的多分类策略和不同核函数对声调识别的影响进行了实验研究。
与BP神经网络相比，支持矢量机具有更高的识别率和更强的推广能力。-This paper presents a novel support vector machine based Chinese tone recognition method．A
new tone recognition feature is first ex血acted using the fundamental frequency(FO)and logarithmic
energy．And how to select the method of SVM multi-class classification and kernel function is also discussed
by experiments．Compared with BP neural network，SVM has higher recognition rates and more strong
generalization． Platform: |
Size: 472064 |
Author:沈 |
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Description: 究基于粗糙集核优化的支持向量机(RS-SVM)在红外光谱定量中的应用。通过粗糙集分类的方
法对多组分污染气体红外光谱对应的特征波长段进行核函数初始数据的优化，再将优化后的核函数带入支
持向量机，从而将二维混合光谱信息投影到高维空间，再进行单种气体浓度的反演运算。通过采用LS-SVM
和PCA-SVM两种典型的光谱数据处理算法作对比，对五种混合气体各组分定馈分析进行比较。当光谱可
分度高时，三种方法的预测值都接近标准值，平均误差接近于0．13；而当光谱町分度低时，RS-SVM的预测
值比前两种更精确，且当待测种类越多时，该方法精度和运算时问的优势越显著。-This paper introduced the application of support vector machines(SVM)regression method based on kernel function
optimized by the rough set in the infrared spectrum quantitative calculation．According to kernel function with the rough set clas—
sification s method．the spectrum data(characteristic wavelength section)is optimized．The kernel function lcads support vector
machines．and the SVM project the two-dimensional room to the multi-dimemional room，and calculate the concentration of ev—
cry kind of gas in multi-component pollution gas．By using tWO kinds of typical spectrum data processing algorithm to make the
contrast．the comparison of five kinds of gaseous mixture various proximate analysis is carried out，and when the spectrum sepa—
rable rate iS high。the predicted values of the three methods approach the Dogmal value，and the average error iS smaller than
O．13：but when the spectrum separable rate is 10W，the RS-SVM predicted value is more precise than the first tWO kinds．Exper—
Platform: |
Size: 327680 |
Author:wanggen |
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Description: Kernel alignment is a good method for choosing the proper kernel parameters in SVM or other kernel based method. Platform: |
Size: 1024 |
Author:Yuhang Zhang |
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Description: 支持向量机方法中也存在着一些亟待解决的问题，主要包括：如何用支持向量机更有效的解决多类分类问题，如何解决支持向量机二次规划过程中存在的瓶颈问题、如何确定核函数以及最优的核参数以保证算法的有效性等。-Support vector machine (SVM) method also exist some problems to be solved, mainly includes: how to use support vector machine (SVM) is more effective to solve many class classification problem, support vector machines (SVM) is how to solve quadratic programming bottleneck problems existing in the process, how to determine the kernel function and the optimal kernel parameters to ensure the effectiveness of the algorithm and so on. Platform: |
Size: 210944 |
Author:mumu |
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Description: SVM-KM工具箱 关于支持向量机核方法的有利工具(svm-km toolbox, a useful tool for support vector machine about kernel method.) Platform: |
Size: 371712 |
Author:Cherry Wang |
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Description: 利用粒子群优化算法对支持向量机中的核函数参数和惩罚参数进行优化是非常有效的手段，可以大大提高鲁棒性。实际过程中读者可通过下载我上传的代码，简单进行修改和阅读附件论文即可快速掌握相关方面的知识，快速使用这一方法。(Particle swarm optimization (PSO) is a very effective method to optimize the kernel function parameters and penalty parameters of SVM, which can greatly improve the robustness. In the actual process, readers can quickly grasp relevant knowledge and use this method by downloading the code I uploaded, simply modifying and reading the attached paper.) Platform: |
Size: 5505024 |
Author:ddyecust@163.com |
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