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
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.
Algorithms for the estimation of a channel whose
impulse response is characterized by a large number of zero
tap coefficients are developed and compared.
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.
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.
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
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.
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).
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.
LPC_durbin-durbin recursion(autocorrelations to lpc coef).
description:
compute predictor coefficients from autocorrelations based on durbin recursion.
