OpenStack Compute (Nova)是一套控制器，用于为单个用户或使用群组启动虚拟机实例。它同样能够用于为包含着多个实例的特定项目设置网络。OpenStack Compute在公共云处理方面堪与Amazon EC2相提并论；而在私有云方面也毫不逊色于VMware的产品。在公共云中，这套管理机制将提供预制的镜像或是为用户创建的镜像提供存储机制，这样用户就能够将镜像以虚拟机的形式启动。
上传时间： 2013-11-18
上传用户：253189838
This applet illustrates the prediction capabilities of the multi-layer perceptrons. It allows to define an input signal on which prediction will be performed. The user can choose the number of input units, Hidden units and output units, as well as the delay between the input series and the predicted output series. Then it is possible to observe interesting prediction properties.
标签： capabilities illustrates multi-layer perceptrons
上传时间： 2015-06-17
上传用户：lnnn30
this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the Hidden states of a nonlinear, non-Gaussian state space model.
标签： a.k.a. bootstrap implement particle
上传时间： 2014-11-10
上传用户：caozhizhi
CHMMBOX, version 1.2, Iead Rezek, Oxford University, Feb 2001 Matlab toolbox for max. aposteriori estimation of two chain Coupled Hidden Markov Models.
标签： aposteriori University CHMMBOX version
上传时间： 2014-01-23
上传用户：rocwangdp
madCollection 2.5.2.6 full source This is not your every day VCL component collection. You won t see many new colored icons in the component palette. My packages don t offer many visual components to play with. Sorry, if you expected that! My packages are about low-level stuff for the most part, with as easy handling as possible. To find the Hidden treasures, you will have to look at the documentation (which you re reading just in the moment). Later I plan on writing some nice demos, but for now the documentation must be enough to get you started.
标签： madCollection collection component source
上传时间： 2014-01-18
上传用户：yoleeson
Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right Hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using Hidden Markov models.
标签： Hidden_Markov_model_for_automatic speech_recognition implements left-right
上传时间： 2016-01-23
上传用户：569342831
If you have programming experience and a familiarity with C--the dominant language in embedded systems--Programming Embedded Systems, Second Edition is exactly what you need to get started with embedded software. This software is ubiquitous, Hidden away inside our watches, DVD players, mobile phones, anti-lock brakes, and even a few toasters. The military uses embedded software to guide missiles, detect enemy aircraft, and pilot UAVs. Communication satellites, deep-space probes, and many medical instruments would have been nearly impossible to create without embedded software.
标签： familiarity programming experience dominant
上传时间： 2013-12-11
上传用户：362279997
本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006.
标签： incremental 编写 神经元网络 算法
上传时间： 2016-09-18
上传用户：litianchu
Inside the C++ Object Model Inside the C++ Object Model focuses on the underlying mechanisms that support object-oriented programming within C++: constructor semantics, temporary generation, support for encapsulation, inheritance, and "the virtuals"-virtual functions and virtual inheritance. This book shows how your understanding the underlying implementation models can help you code more efficiently and with greater confidence. Lippman dispells the misinformation and myths about the overhead and complexity associated with C++, while pointing out areas in which costs and trade offs, sometimes Hidden, do exist. He then explains how the various implementation models arose, points out areas in which they are likely to evolve, and why they are what they are. He covers the semantic implications of the C++ object model and how that model affects your programs.
上传时间： 2013-12-24
上传用户：zhouli
Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％ ％ The activation functions must be either linear or tanh. The network ％ architecture is defined by the matrix NetDef consisting of two ％ rows. The first row specifies the Hidden layer while the second ％ specifies the output layer. ％
标签： back-propagation corresponding input-output algorithm
上传时间： 2016-12-27
上传用户：exxxds
