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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.
標簽：
demonstrates
sequential
Selection
Bayesian
上傳時間：
2016-04-07
上傳用戶：lindor
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模式識別學習綜述.該論文的英文參考文獻為303篇.很有可讀價值.Abstract— Classical and recent results in statistical pattern
recognition and learning theory are reviewed in a two-class
pattern classification setting. This basic model best illustrates
intuition and analysis techniques while still containing the essential
features and serving as a prototype for many applications.
Topics discussed include nearest neighbor, kernel, and histogram
methods, Vapnik–Chervonenkis theory, and neural networks. The
presentation and the large (thogh nonexhaustive) list of references
is geared to provide a useful overview of this field for both
specialists and nonspecialists.
標簽：
statistical
Classical
Abstract
pattern
上傳時間：
2013-11-25
上傳用戶：www240697738
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In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標簽：
Rauch-Tung-Striebel
algorithm
smoother
which
上傳時間：
2016-04-15
上傳用戶：zhenyushaw
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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.
標簽：
sequential
reversible
algorithm
nstrates
上傳時間：
2014-01-18
上傳用戶：康郎
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本人編寫的incremental 隨機神經元網絡算法，該算法最大的特點是可以保證approximation特性，而且速度快效果不錯，可以作為學術上的比較和分析。目前只適合benchmark的regression問題。
具體效果可參考
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.
標簽：
incremental
編寫
神經元網絡
算法
上傳時間：
2016-09-18
上傳用戶：litianchu
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The goal of this thesis is the development of traffic engineering rules for cellular packet
radio networks based on GPRS and EDGE. They are based on traffic models for typical
mobile applications. Load generators, representing these traffic models, are developed
and integrated into a simulation environment with the prototypical implementation of
the EGPRS protocols and models for the radio channel, which were also developed in
the framework of this thesis. With this simulation tool a comprehensive performance
evaluation is carried out that leads to the traffic engineering rules.
標簽：
development
engineering
cellular
traffic
上傳時間：
2014-01-11
上傳用戶：Miyuki
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Pattern Analysis is the process of fi nding general relations in a set of data,
and forms the core of many disciplines, from neural networks to so-called syn-
tactical pattern recognition, from statistical pattern recognition to machine
learning and data mining. Applications of pattern analysis range from bioin-
formatics to document retrieval.
標簽：
the
relations
Analysis
Pattern
上傳時間：
2017-09-07
上傳用戶：SimonQQ
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The wireless market has experienced a phenomenal growth since the first second-
generation (2G) digital cellular networks, based on global system for mobile
communications (GSM) technology, were introduced in the early 1990s. Since then,
GSM has become the dominant global 2G radio access standard. Almost 80% of today’s
new subscriptions take place in one of the more than 460 cellular networks that use
GSM technology. This growth has taken place simultaneously with the large experienced
expansion of access to the Internet and its related multimedia services.
標簽：
Performance
Evolution
GPRS
EDGE
GSM
and
上傳時間：
2020-05-27
上傳用戶：shancjb
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In this research, we have designed, developed implemented a wireless sensor
networks based smart home for safe, sound and secured living environment for
any inhabitant especially elderly living alone. We have explored a methodology
for the development of efficient electronic real time data processing system to
recognize the behaviour of an elderly person. The ability to determine the
wellness of an elderly person living alone in their own home using a robust,
flexible and data driven artificially intelligent system has been investigated. A
framework integrating temporal and spatial contextual information for
determining the wellness of an elderly person has been modelled. A novel
behaviour detection process based on the observed sensor data in performing
essential daily activities has been designed and developed.
標簽：
Smart
Homes
上傳時間：
2020-06-06
上傳用戶：shancjb
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Recent years have seen a rapid development of neural network control tech-
niques and their successful applications. Numerous simulation studies and
actual industrial implementations show that artificial neural network is a good
candidate for function approximation and control system design in solving the
control problems of complex nonlinear systems in the presence of different kinds
of uncertainties. Many control approaches/methods, reporting inventions and
control applications within the fields of adaptive control, neural control and
fuzzy systems, have been published in various books, journals and conference
proceedings.
標簽：
Stable_adaptive_neural_network_co
ntrol
上傳時間：
2020-06-10
上傳用戶：shancjb
