Description: 最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, \"The Nature of Statistical Learning Theory\", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, \"Fast training of support vector machines using sequential minimal optimization\", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, \"Estimating the Generalization Performance of a SVM Efficiently\", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it. Platform: |
Size: 172130 |
Author:金星 |
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Description: 最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it. Platform: |
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Author:金星 |
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Description: 非平衡数据集的分类问题经常出现在许多实际应用中.支持向量机在处理这一类问题时,整体分类性能比较低.为此,Veropoulos提出的采用不同惩罚系数的改进算法可以较好的解决此类问题.此外,可以利用序列最小优化算法简单快速的解决上述优化问题.-Non-equilibrium data sets classification problems often appear in many practical applications. Support Vector Machine in dealing with this type of issue, the overall classification performance is relatively low. To this end, Veropoulos proposed punishment coefficient using different algorithms can improve the better solution such issues. In addition, can use the Sequential Minimal Optimization algorithm is simple and fast solution to the above optimization problem. Platform: |
Size: 19456 |
Author:苏苏 |
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Description: 支持向量机（svm）的 序列最小优化算法（sequential minimal optimization) 的大量论文资料！该算法可用来开发svm的并行算法！-Support vector machine (svm) of the sequential minimal optimization algorithm (sequential minimal optimization) information on a large number of papers! The algorithm can be used to develop a parallel algorithm for svm! Platform: |
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Author:lujia |
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Description: 实现序贯最小优化算法，该算法可加速求解支持向量机问题-To achieve sequential minimal optimization algorithm that can accelerate the problem solving support vector machines Platform: |
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Author:赵亮 |
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Description: 支持向量机（SVM）是一种基于超平面分类的新的学习方法，具有很强的泛化能力。研究了支持向量机的学习机理，以及实现支持向量机的序贯最小优化算法（SMO），并用来对舰船图像进行识别。首先将待识别目标进行二维小波分解，获取不同尺度下的小波系数，然后对其进行主元分析，得到的主元分量作为支持向量机的特征量输入。实验结果表明，该方法具有良好的分类性能。-Support Vector Machine (SVM) is a hyperplane-based classification of new learning method, has strong generalization ability. Of support vector machine learning mechanism, and the realization of support vector machines Sequential Minimal Optimization (SMO), and used to identify the image on the ship. Targets to be identified first two-dimensional wavelet decomposition, for different scales of wavelet coefficients, and then carry out the main element of their analysis, the main element component as the feature of SVM input. Experimental results show that the method has good classification performance. Platform: |
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Author:罗朝辉 |
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Description: SMO算法由Microsoft Research的John C. Platt在1998年提出，并成为最快的二次规划优化算法，特别针对线性SVM和数据稀疏时性能更优。关于SMO最好的资料就是他本人写的《Sequential Minimal Optimization A Fast Algorithm for Training Support Vector Machines》了。-This paper proposes a new algorithm for training support vector machines: Sequential
Minimal Optimization, or SMO. Training a support vector machine requires the solution o
a very large quadratic programming (QP) optimization problem. SMO breaks this large
QP problem into a series of smallest possible QP problems. These small QP problems are
solved analytically, which avoids using a time-consuming numerical QP optimization as a
inner loop. The amount of memory required for SMO is linear in the training set size,
which allows SMO to handle very large training sets. Because matrix computation is
avoided, SMO scales somewhere between linear and quadratic in the training set size for
various test problems, while the standard chunking SVM algorithm scales somewhere
between linear and cubic in the training set size. SMO’s computation time is dominated b
SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. On real-
world sparse data sets, SMO can be mor Platform: |
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Author:高飞 |
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Description: 序贯最小优化：
训练支持向量机的一种快速算法。本文提出了一种新的支持向量机训练算法：顺序
最小优化，或SMO。-Sequential Minimal Optimization:
A Fast Algorithm for Training Support Vector Machines.This paper proposes a new algorithm for training support vector machines: Sequential
Minimal Optimization, or SMO. Platform: |
Size: 76800 |
Author:chenxiaoguai |
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Description: 序列最小优化训练支持基于向量机的一种序列最小优化训练的快速算法。-Sequential Minimal Optimization- A Fast Algorithm for Training Support Vector Machines Platform: |
Size: 76800 |
Author:Mr.W |
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Search - Sequential minimal optimization