Multidimensional SCALING in matlab by Mark Steyvers 1999 ％needs optimization toolbox ％Modified by Bruce Land ％--Data via globals to anaylsis programs ％--3D plotting with color coded groups ％--Mapping of MDS space to spike train temporal profiles as described in ％Aronov, et.al. "Neural coding of spatial phase in V1 of the Macaque" in ％press J. Neurophysiology
標(biāo)簽： Multidimensional optimization Modified Steyvers
上傳時(shí)間： 2015-08-26
上傳用戶：kytqcool
The CUBA library provides new implementation of four general-purpose Multidimensional integration algorithms: Vegas, Suave, Divonne, and Cuhre. Suave is a new algorithm, Divonne is a known algorithm to which important details have been added, and Vegas and Cuhre are new implementations of existing algorithms with only few improvements over the original versions. All four algorithms can integrate vector integrands and have very similar Fortran, C/C++, and Mathematica interfaces.
標(biāo)簽： Multidimensional general-purpose implementation integration
上傳時(shí)間： 2014-09-09
上傳用戶：gxf2016
I developed an algorithm for using local ICA in denoising Multidimensional data. It uses delay embedded version of the data, clustering and ICA for the separation between data and noise.
標(biāo)簽： Multidimensional developed algorithm denoising
上傳時(shí)間： 2016-06-01
上傳用戶：cc1915
％ EM algorithm for k Multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％
標(biāo)簽： Multidimensional estimation algorithm Gaussian
上傳時(shí)間： 2013-12-03
上傳用戶：我們的船長(zhǎng)
BASIC Multidimensional GRADIENT METHODS，主要用于解決最優(yōu)化問題
標(biāo)簽： Multidimensional GRADIENT METHODS BASIC
上傳時(shí)間： 2016-07-01
上傳用戶：z754970244
Multidimensional Optimization 統(tǒng)計(jì)算法
標(biāo)簽： Multidimensional Optimization 算法
上傳時(shí)間： 2013-12-24
上傳用戶：541657925
Many problems in statistical pattern recognition begin with the preprocessing of Multidimensional signals, such as images of faces or spectrograms of speech.
標(biāo)簽： Multidimensional preprocessing statistical recognition
上傳時(shí)間： 2016-12-01
上傳用戶：515414293
流分類中的一種算法：Packet Classification Using Multidimensional Cutting
標(biāo)簽： Multidimensional Classification Cutting Packet
上傳時(shí)間： 2014-12-04
上傳用戶：天涯
The Spectral Toolkit is a C++ spectral transform library written by Rodney James and Chuck Panaccione while at the National Center for Atmospheric Research between 2002 and 2005. The library contains a functional subset of FFTPACK and SPHEREPACK, including real and complex FFTs in 1-3 dimensions, and a spherical harmonic transform. Multithreading is supported through POSIX threads for the Multidimensional transforms. This reference guide provides details of the public interface as well as the internal implementation of the library.
標(biāo)簽： Panaccion transform Spectral spectral
上傳時(shí)間： 2013-12-20
上傳用戶：haoxiyizhong
非常好的優(yōu)化算法的書，詳細(xì)介紹了蟻群算法和粒子群算法以及相關(guān)的matlab工具箱，講了理論和應(yīng)用給出了工具箱的下載地址。 Swarm intelligence is an innovative computational way to solve hard problems. In particular, particle swarm optimization, also commonly known as PSO, mimics the behavior of a swarm of insects or a school of fish. If one of the particle discovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in Multidimensional space that have two characteristics: a position and a velocity. These particles wander around the hyperspace and remember the best position that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions.
標(biāo)簽： 優(yōu)化算法
上傳時(shí)間： 2014-01-26
上傳用戶：zgu489
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