The IA-32 Intel Architecture Software Developer’s Manual, Volume 1: Basic Architecture (Order Number 245470) is part of a three-volume set that describes the architecture and programming environment of all IA-32 Intel Architecture processors.
標簽： Architecture Developer Software Manual
上傳時間： 2016-04-08
上傳用戶：妄想演繹師
The IA-32 Intel Architecture Software Developer’s Manual, Volume 2: Instruction Set Reference (Order Number 245471) is part of a three-volume set that describes the architecture and programming environment of all IA-32 Intel® Architecture processors. the IA-32 Intel Architecture Software Developer’s Manual, Volume 2, describes the instructions set of the processor and the opcode structure. These two volumes are aimed at application programmers who are writing programs to run under existing operating systems or executives.
標簽： Architecture Instruction Developer Reference
上傳時間： 2013-12-15
上傳用戶：xsnjzljj
The IA-32 Software Developer’s Manual, Volume 3: System Programming Guide (Order Number 245472), is part of a three-volume set that describes the architecture and programming environment of all IA-32 Intel® Architecture processors. The IA-32 Software Developer’s Manual, Volume 3, describes the operating-system support environment of an IA-32 processor, including memory management, protection, task management, interrupt and exception handling, and system management mode. It also provides IA-32 processor compatibility information. This volume is aimed at operating- system and BIOS designers and programmers.
標簽： Programming Developer Software 245472
上傳時間： 2013-12-23
上傳用戶：小碼農lz
程序採用了無狀態連接池的三層結構，通用函數打包在DLL中，取數與回添都有一些技巧，全部通過自己編寫的函數實現infopower，1stclass，FastReport2.52, 以及一個“liuxiangvcl”包含在component中.
上傳時間： 2013-12-19
上傳用戶：wang0123456789
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal ％ component subspace U of dimension PPCA_DIM using a centred covariance matrix X. The variable VAR contains the off-subspace variance (which is assumed to be spherical), while the vector LAMBDA contains the variances of each of the principal components. This is computed using the eigenvalue and eigenvector decomposition of X.
標簽： Probabilistic Components Principal Analysis
上傳時間： 2016-04-28
上傳用戶：qb1993225
A Web Tutorial on Discrete Features of Bayes Decision Theory This applet allows for the calculation of the decision boundary given a three dimensional feature vector. Specifically, by stipulating the variables such as the priors, and the conditional likelihoods of each feature with respect to each class, the changing decision boundary will be displayed.
標簽： calculation Tutorial Discrete Decision
上傳時間： 2013-12-22
上傳用戶：hxy200501
pMatlab is a toolsbox from MIT for running matlab in parallel style on a multi-core PC or a cluster environment. These two documents summary the usage of pMatlab and running time measurements on three simple Monte Carlo simulation codes.
標簽： multi-core toolsbox parallel pMatlab
上傳時間： 2014-12-05
上傳用戶：zhliu007
This document including C language project file organization, C language trap and flaw, C language programming precious book three parts.
標簽： language organization including document
上傳時間： 2016-05-28
上傳用戶：tfyt
The FastICA package is a free (GPL) MATLAB program that implements the fast fixed-point algorithm for independent component analysis and projection pursuit. It features an easy-to-use graphical user interface, and a computationally powerful algorithm.
標簽： fixed-point implements algorithm FastICA
上傳時間： 2014-08-17
上傳用戶：yy541071797
JLAB is a set of Matlab functions I have written or co-written over the past fifteen years for the purpose of analyzing data. It consists of four hundred m-files spanning thirty thousand lines of code. JLAB includes functions ranging in complexity from one-line aliases to high-level algorithms for certain specialized tasks. These have been collected together and made publicly available for you to use, modify, and --- subject to certain very reasonable constraints --- to redistribute. Some of the highlights are: a suite of functions for the rapid manipulation of multi-component, potentially multi-dimensional datasets a systematic way of dealing with datasets having components of non-uniform length tools for fine-tuning figures using compact, straightforward statements and specialized functions for spectral and time / frequency analysis, including advanced wavelet algorithms developed by myself and collaborators.
標簽： co-written functions the fifteen
上傳時間： 2014-01-26
上傳用戶：hjshhyy
