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aic
数子信号处理中的信源数目估计aic的MATLAB程序(The number of sub-signal processing in the estimated number of the letter of the source of the MATLAB program aic)
- 2009-12-01 22:50:46下载
- 积分：1
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FYP
this code about optimization
- 2013-08-15 02:39:47下载
- 积分：1
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fisherface
Eigenfaces: PCA tends to find a p-dimensional subspace
whose basis vectors correspond to the maximum
variance direction in the original image space (p N).
We called the new subspace defined by basis vectors “face
space”. First, all training faces are projected onto the face
space to find a set of weights that describes the contribution
of each vector. Then we project all testing faces onto the
face space to obtain a set of weights. Finally, we identify
the face by comparing a set of weights for the testing face
to sets of weights of training faces.
- 2010-11-20 17:00:21下载
- 积分：1
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1d-photonic-crystal
说明： 按照TE/TM模，研究一维光子晶体输出特性(In accordance with the TE/TM mode to study the one-dimensional photonic crystal output characteristics)
- 2008-10-16 17:00:40下载
- 积分：1
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IsInsideThePolygon
Given a point, it finds whether the point is inside or outside of the polygon, given polygon vertices.
- 2011-09-23 00:15:42下载
- 积分：1
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zimutongji
运行文件后任意输入字母，可以统计大小写字母的个数(After the input file to run arbitrary letters, upper and lower case letters can be the number of statistics)
- 2008-06-06 17:54:49下载
- 积分：1
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exercise
Sine pdf for users that work in the domain of signal processing. help how to improve the process and work
- 2014-11-19 22:34:48下载
- 积分：1
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the-file-of--signal-and-system
关于信号与系统的习题MATLAB编程，希望对大家有用。(some files about signal and system )
- 2013-04-24 09:11:03下载
- 积分：1
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Relevance-Vector-Machine
说明： 相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。
它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method.
Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)
- 2021-03-23 21:20:53下载
- 积分：1
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three_phase_vsc
This is modeling three phase VSC
- 2021-04-28 21:58:43下载
- 积分：1
