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Description: 支持向量机的matlab实现，包括线性和非线性算法，以及介绍使用的说明文件。
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Size: 212502 |
Author: 邵桂芳 |
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Description: 此源码提供了ssvm的完整的matlab源代码，主要运用UD设计原理对参数搜索提高了效率。
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Size: 1182900 |
Author: 刘娟 |
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Description: 光滑支持向量机程序,用于识别两分类问题,有很好的发展前景,有兴趣可以-Smooth support vector machine procedures used to identify the two classification problems, have good development prospects are interested in can
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Size: 4096 |
Author: mawentao |
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Description: 支持向量机的matlab实现，包括线性和非线性算法，以及介绍使用的说明文件。-Support vector machine matlab realize, including linear and nonlinear algorithms, and introduce the use of documentation.
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Size: 211968 |
Author: 邵桂芳 |
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Description: 此源码提供了ssvm的完整的matlab源代码，主要运用UD设计原理对参数搜索提高了效率。-This source provides a complete ssvm the matlab source code, the main design principle of the use of UD on the parameters of the search efficiency.
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Size: 1182720 |
Author: 刘娟 |
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Description: 本程序计算局部窗口的累积直方图，可用于驱动水平集和纹理分割- in this test program, we calculate the cumulative histogram in a local
window centered at each pixel,this local cumulative histogram can be
used to drive the level set for image and texture segmentation.
Author: Associate Prof. Yuanquan Wang,
Affiliation: Tianjin Key Lab of Intelligent Computing and Novel Software Technology,
School of Computer Science, Tianjin University of Technology, Tianjin 300191, China
01/20/2008
Reference:
1. Tony Chan, Selim Esedoglu, and Kangyu Ni, Histogram Based Segmentation Using Wasserstein Distances, SSVM 2007, LNCS 4485, pp. 697–708, 2007.
2. Kangyu Ni, Xavier Bresson, Tony Chan, Selim Esedog, Local Histogram based Segmentation using the Wasserstein Distance,
at: www.math.lsa.umich.edu/~esedoglu/Papers_Preprints/chan_esedoglu_ni.pdf
or at :ftp://ftp.math.ucla.edu/pub/camreport/cam08-47.pdf
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Size: 2048 |
Author: 方可 |
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Description: 嵌入维数自适应最小二乘支持向量机
状态时间序列预测方法
Condition Time Series Prediction Using Least Squares Support Vector Machine
with Adaptive Embedding Dimension
针对航空发动机状态时间序列预测中嵌入维数难于有效选取的问题, 提出一种基于嵌入维数自适应
最小二乘支持向量机( L SSVM ) 的预测方法。该方法将嵌入维数作为影响状态时间序列预测精度的重要参
数, 以交叉验证误差为评价准则, 利用粒子群优化( P SO ) 进化搜索LSSV M 预测模型的最优超参数与嵌入维
数, 同时通过矩阵变换原理提高交叉验证过程的计算效率, 并最终建立优化后的L SSVM 预测模型。航空发
动机排气温度( EGT ) 预测实例表明, 该方法可自适应选取适用于状态时间序列预测的最优嵌入维数且预测
精度高, 适用于航空发动机状态时间序列预测。- T o deal wit h the difficulty of selecting an appro pr iate embedding dimension for aeroeng ine co ndition
time series predictio n, a metho d based o n least squar es suppo rt vecto r machine ( L SSVM ) with ada ptive em
bedding dimension is pro po sed. I n the method, the embedding dimensio n is identified as a parameter that af
fects the accuracy o f the aer oengine condition time series predictio n par ticle sw arm o ptimizat ion ( P SO) is ap
plied to optimize the hyperpar ameter s and embedding dimension of the L SSV M pr edict ion model cro ssv alida
tion is applied to evaluate the perfo rmance o f the L SSVM predictio n mo del and matr ix tr ansfo rm is applied to
the L SSVM pr ediction model tr aining to accelerate the crossvalidation evaluation pro cess. Ex periments on an
aeroengine ex haust g as t emperatur e ( EGT ) predictio n demonst rates that the metho d is hig hly effective in em
bedding dimension selection. In compar ison w ith co nv
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