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The data files included are .MAT or *.dat (ASCII)files. The m-files and the
data may be distributed, provided that the source is acknowledged in any
publication and the data are not sold. Since this software is being distributed
free of charge, the authors are not offering any technical support. Students who
have any questions or difficulties using this software, or require the
additional functions from the Signal Processing Toolbox should contact their
professor.
標(biāo)簽：
files
data
distributed
The
上傳時(shí)間：
2014-12-06
上傳用戶：wuyuying
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Written by the inventors of the technology, The Java™ Language Specification, Third Edition, is the definitive technical reference for the Java™ programming language. If you want to know the precise meaning of the language s constructs, this is the source for you.
The book provides complete, accurate, and detailed coverage of the Java programming language. It provides full coverage of all new features added since the previous edition, including generics, annotations, asserts, autoboxing, enums, for-each loops, variable arity methods, and static import clauses.
標(biāo)簽：
Specification
technology
the
inventors
上傳時(shí)間：
2016-01-26
上傳用戶：youmo81
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During this course you will learn how to use Matlab, to design, and to perform mathematical computations.
You will also get acquainted with basic programming. If you learn to use this program well, you will find it
very useful in future, since many technical or mathematical problems can be solved using Matlab.
標(biāo)簽：
mathematical
computat
to
perform
上傳時(shí)間：
2014-01-15
上傳用戶：Miyuki
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THIS book covers the Java™ Native Interface (JNI). It will be useful to you if
you are interested in any of the following:
• integrating a Java application with legacy code written in languages such as C
or C++
• incorporating a Java virtual machine implementation into an existing application
written in languages such as C or C++
• implementing a Java virtual machine
• understanding the technical issues in language interoperability, in particular
how to handle features such as garbage collection and multithreading
標(biāo)簽：
Interface
you
interes
Native
上傳時(shí)間：
2013-12-12
上傳用戶：ljmwh2000
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DESIGN PATTERNS JAVA COMPANION
Design patterns began to be recognized more formally in the early
1990s by Helm (1990) and Erich Gamma (1992), who described patterns
incorporated in the GUI application framework, ET++. The culmination of
these discussions and a number of technical meetings was the publication of
the parent book in this series, Design Patterns -- Elements of Reusable
Software, by Gamma, Helm, Johnson and Vlissides.(1995). This book,
commonly referred to as the Gang of Four or “GoF” book, has had a powerful
impact on those seeking to understand how to use design patterns and has
become an all-time best seller. We will refer to this groundbreaking book as
Design Patterns, throughout this book and The Design Patterns Smalltalk
Companion (Alpert, Brown and Woolf, 1998) as the Smalltalk Companion.
標(biāo)簽：
recognized
COMPANION
PATTERNS
patterns
上傳時(shí)間：
2016-02-27
上傳用戶：大三三
-
This paper provides incumbent wireless Internet service providers (WISPs), new WISPs and
demanding new markets (such as government and education) with a technical analysis of
alternatives for implementing last-mile wireless broadband services.
標(biāo)簽：
WISPs
incumbent
demanding
providers
上傳時(shí)間：
2014-01-05
上傳用戶：二驅(qū)蚊器
-
On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標(biāo)簽：
demonstrates
sequential
Selection
Bayesian
上傳時(shí)間：
2016-04-07
上傳用戶：lindor
-
EclipseTrader is a stock exchange analysis
system, featuring shares pricing watch, intraday and history charts with
technical analysis indicators, level II/market depth view, news watching,
automated trading systems, integrated trading.
標(biāo)簽：
EclipseTrader
featuring
exchange
analysis
上傳時(shí)間：
2016-04-13
上傳用戶：shizhanincc
-
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標(biāo)簽：
sequential
reversible
algorithm
nstrates
上傳時(shí)間：
2014-01-18
上傳用戶：康郎
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This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標(biāo)簽：
reversible
algorithm
the
nstrates
上傳時(shí)間：
2014-01-08
上傳用戶：cuibaigao
