support vector classification machine ％ soft margin ％ uses "kernel.m" ％ ％ xtrain: (Ltrain,N) with Ltrain: number of points N: dimension ％ ytrain: (Ltrain,1) containing class labels (-1 or +1) ％ xrun: (Lrun,N) with Lrun: number of points N: dimension ％ atrain: alpha COEFFICIENTS (from svcm_train on xtrain and ytrain) ％ btrain: offest coefficient (from svcm_train on xtrain and ytrain) ％ ％ ypred: predicted y (Lrun,1) containing class labels (-1 or +1) ％ margin: (signed) separation from the separating hyperplane (Lrun,1
標簽： classification support machine Ltrain
上傳時間： 2015-09-04
上傳用戶：問題問題
A new blind adaptive multiuser detection scheme based on a hybrid of Kalman filter and subspace estimation is proposed. It is shown that the detector can be expressed as an anchored signal in the signal subspace and the COEFFICIENTS can be estimated by the Kalman filter using only the signature waveform and the timing of the desired user.
標簽： multiuser detection adaptive subspace
上傳時間： 2015-09-07
上傳用戶：xieguodong1234
Algorithms for the estimation of a channel whose impulse response is characterized by a large number of zero tap COEFFICIENTS are developed and compared.
標簽： characterized Algorithms estimation response
上傳時間： 2013-12-26
上傳用戶：manking0408
多項式曲線擬合 任意介數 Purpose - Least-squares curve fit of arbitrary order working in C++ Builder 2007 as a template class, using vector<FloatType> parameters. Added a method to handle some EMathError exceptions. If do NOT want to use this just call PolyFit2 directly. usage: Call PolyFit by something like this. CPolyFit<double> PolyFitObj double correlation_coefficiant = PolyFitObj.PolyFit(X, Y, A) where X and Y are vectors of doubles which must have the same size and A is a vector of doubles which must be the same size as the number of COEFFICIENTS required. returns: The correlation coefficient or -1 on failure. produces: A vector (A) which holds the COEFFICIENTS.
標簽： Least-squares arbitrary Purpose Builder
上傳時間： 2013-12-18
上傳用戶：宋桃子
This folder has some scritps that you may find usefull. All of it comes from questions that I ve received in my email. If you have a new request/question, feel free to send it to marceloperlin@gmail.com. But please, don t ask me to do your homework. Passing_your_param0 This folder contains instructions (and m files) for passing you own initial parameters to the fitting function. I also included a simple simulation script that will create random initial COEFFICIENTS (under the proper bounds) and fit the model to the data.
標簽： that questions scritps usefull
上傳時間： 2013-12-28
上傳用戶：netwolf
This directory includes matlab interface of the curvelet transform using usfft. Basic functions fdct_usfft.m -- forward curvelet transform afdct_usfft.m -- adjoint curvelet transform ifdct_usfft.m -- inverse curvelet transform fdct_usfft_param.m -- returns the location of each curvelet in phase-space Useful tools fdct_usfft_dispcoef.m -- returns a matrix contains all curvelet COEFFICIENTS fdct_usfft_pos2idx.m -- for fixed scale and fixed direction, returns the curvelet which is closest to a certain point on the image Demos fdct_usfft_demo_basic.m -- display the shape of a curvelet fdct_usfft_demo_recon.m -- partial reconstruction using curvelet fdct_usfft_demo_disp.m -- display all the curvelet COEFFICIENTS of an image fdct_usfft_demo_denoise.m -- image denoising using curvelet
標簽： directory functions interface transform
上傳時間： 2016-08-31
上傳用戶：cooran
Produce Java classes to calculate and display the root of a quadratic equation when input the COEFFICIENTS a, b and c within the range of -100 to 100 by user.
標簽： calculate the quadratic equation
上傳時間： 2014-01-17
上傳用戶：aappkkee
PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A components) those variables that do not carry any relevant information to model Y. The criterion used to trace the un-informative variables is the reliability of the regression COEFFICIENTS: c_j=mean(b_j)/std(b_j), obtained by jackknifing. The cutoff level, below which c_j is considered to be too small, indicating that the variable j should be removed, is estimated using a matrix of random variables.The predictive power of PLS models built on the retained variables only is evaluated over all 1-a dimensions =(yielding RMSECVnew).
標簽： from eliminates PRINCIPLE algorithm
上傳時間： 2016-11-27
上傳用戶：凌云御清風
A cylindrical wave expansion method is developed to obtain the scattering field for an ideal two-dimensional cylindrical invisibility cloak. A near-ideal model of the invisibility cloak is set up to solve the boundary problem at the inner boundary of the cloak shell. We confirm that a cloak with the ideal material parameters is a perfect invisibility cloak by systematically studying the change of the scattering COEFFICIENTS from the near-ideal case to the ideal one. However, due to the slow convergence of the zeroth order scattering COEFFICIENTS, a tiny perturbation on the cloak would induce a noticeable field scattering and penetration.
標簽： cylindrical scattering expansion developed
上傳時間： 2017-03-30
上傳用戶：lhc9102
LPC_durbin-durbin recursion(autocorrelations to lpc coef). description: compute predictor COEFFICIENTS from autocorrelations based on durbin recursion.
標簽： LPC_durbin-durbin autocorrelations description recursion
上傳時間： 2014-08-25
上傳用戶：weiwolkt
