Core Java 2 Volume I-Fundamentals 5th Edition。非常經(jīng)典的Java2 核心技術(shù)（卷1）。-Core Java 2 Volume I-Fundamentals 5th Edition. Extremely classical Java2 core technology (volume 1). ,Java,Java書(shū)籍/Java Books
標(biāo)簽： I-Fundamentals Volume Core Java
上傳時(shí)間： 2014-08-07
上傳用戶(hù)：離殤
數(shù)值線(xiàn)性代數(shù)的Matlab應(yīng)用程序包 共13個(gè)程序函數(shù)，每個(gè)程序函數(shù)有相應(yīng)的例子函數(shù)一一對(duì)應(yīng)，以*Example.m命名 程序名稱(chēng) 用途 Method 方法 GrmSch.m QR因子分解 classical Gram－Schmidt orthogonalization 格拉母－斯密特 MGrmSch.m QR因子分解 modified Gram－Schmidt iteration 修正格拉母－斯密特 householder.m QR因子分解 Householder 豪斯霍爾德QR因子分解 ZXEC.m 最小二乘擬合 polynomial interpolant 最小二乘插值多項(xiàng)式 NCLU.m LU因子分解 Gaussian elimination 不選主元素的高斯消元 PALU.m LU因子分解 partial pivoting Gaussian elimination 部分選主元的高斯消元 cholesky.m 楚因子分解 Cholesky Factorization 楚列斯基因子分解 PwItrt.m 求最大特征值 Power Iteration 冪迭代 Jacobi.m 求特征值 Jacobi iteration 按標(biāo)準(zhǔn)行方式次序的雅可比算法 Anld.m 求上Hessenberg Arnoldi Iteration 阿諾爾迪迭代 zuisu.m 解線(xiàn)性方程組 Steepest descent 最速下降法 CG.m 解線(xiàn)性方程組 Gradients 共軛梯度 BCG.m 解線(xiàn)性方程組 Biconjugate Gradients 雙共軛梯度
標(biāo)簽： Matlab 數(shù)值 應(yīng)用程序 函數(shù)
上傳時(shí)間： 2016-05-17
上傳用戶(hù)：小鵬
GloptiPoly 3: moments, optimization and semidefinite programming. Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem of Moments (GPM), an infinite-dimensional optimization problem which can be viewed as an extension of the classical problem of moments [8]. From a theoretical viewpoint, the GPM has developments and impact in various areas of mathematics such as algebra, Fourier analysis, functional analysis, operator theory, probability and statistics, to cite a few. In addition, and despite a rather simple and short formulation, the GPM has a large number of important applications in various fields such as optimization, probability, finance, control, signal processing, chemistry, cristallography, tomography, etc. For an account of various methodologies as well as some of potential applications, the interested reader is referred to [1, 2] and the nice collection of papers [5].
標(biāo)簽： optimization semidefinite programming GloptiPoly
上傳時(shí)間： 2016-06-05
上傳用戶(hù)：lgnf
This program is a new way to estimate the coherence function. It s based on the MVDR and is much more reliable than the classical Welch s method implemented in MATLAB. There are 2 programs: the main program called coherence_MVDR.m and and an example, called illustrate.m, that calls the main function to show how it works. There also included 2 papers that we published on this algorithm.
標(biāo)簽： coherence the estimate function
上傳時(shí)間： 2016-07-03
上傳用戶(hù)：athjac
the code environment s C++. it is in image processing field. this code Read TIFF Image then it excute a Median Filter classical on gray images. after that it Write TIFFU nsigned Char.
標(biāo)簽： code environment processing Image
上傳時(shí)間： 2016-12-24
上傳用戶(hù)：xiaoxiang
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
標(biāo)簽： foundations The consists sections
上傳時(shí)間： 2017-06-22
上傳用戶(hù)：lps11188
This article describes a new efficient implementation of the Cooley-Tukey fast Fourier transform (FFT) algorithm using C++ template metaprogramming. Thank to the recursive nature of the FFT, the source code is more readable and faster than the classical implementation. The efficiency is proved by performance benchmarks on different platforms.
標(biāo)簽： implementation Cooley-Tukey describes efficient
上傳時(shí)間： 2013-12-23
上傳用戶(hù)：netwolf
Matlab 畫(huà)三維立體圖形 The aim of geom3d library is to handle and visualize 3D geometric primitives such as points, lines, planes, polyhedra... It provides low-level functions for manipulating 3D geometric primitives, making easier the development of more complex geometric algorithms. Some features of the library are: - creation of various shapes (3D points, 3D lines, planes, polyhedra...) through an intuitive syntax. Ex: createPlane(p1, p2, p3) to create a plane through 3 points. - derivation of new shapes: intersection between 2 planes, intersection between a plane and a line, between a sphere and a line... - functions for 3D polygons and polyhedra. Polyhedra use classical vertex-faces arrays (face array contain indices of vertices), and support faces with any number of vertices. Some basic models are provided (createOctaedron, createCubeoctaedron...), as well as some computation (like faceNormal or centroid) - manipulation of planar transformation. Ex.: ROT = createRotationOx(THETA); P2 = transformPoint3d(P1, ROT); - direct drawing of shapes with specialized functions. Clipping is performed automatically for infinite shapes such as lines or rays. Ex: drawPoint3d([50 50 25; 20 70 10], 'ro'); % draw some points drawLine3d([X0 Y0 Z0 DX DY DZ]); % clip and draw straight line Some functions require the geom2d package. Additional help is provided in geom3d/Contents.m file, as well as summary files like 'points3d.m' or 'lines3d.m'.
標(biāo)簽： Matlab 畫(huà)三維立體圖形
上傳時(shí)間： 2015-11-02
上傳用戶(hù)：A1321
In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
標(biāo)簽： recognition Bi-density machines support pattern vector twin for
上傳時(shí)間： 2019-06-09
上傳用戶(hù)：lyaiqing
In recent years, the research and developments in the area of RF and microwave technologies have progressed significantly due to the growing demand for applica- bility in wireless communication technologies. Starting from 1992, wireless com- munication technologies have become quite mature. In the modern era of electronic developments, design of wireless handsets is an example of integration of many di- verse skill sets. classical books in the areas of microwave technology provide us with an in-depth knowledge of electromagnetic fundamentals.
標(biāo)簽： Front-Ends Receiver Modern
上傳時(shí)間： 2020-05-31
上傳用戶(hù)：shancjb
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