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
模式識(shí)別學(xué)習(xí)綜述.該論文的英文參考文獻(xiàn)為303篇.很有可讀價(jià)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(biāo)簽： statistical Classical Abstract pattern
上傳時(shí)間： 2013-11-25
上傳用戶：www240697738
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(biāo)簽： Rauch-Tung-Striebel algorithm smoother which
上傳時(shí)間： 2016-04-15
上傳用戶：zhenyushaw
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
本人編寫(xiě)的incremental 隨機(jī)神經(jīng)元網(wǎng)絡(luò)算法，該算法最大的特點(diǎn)是可以保證approximation特性，而且速度快效果不錯(cuò)，可以作為學(xué)術(shù)上的比較和分析。目前只適合benchmark的regression問(wèn)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(biāo)簽： incremental 編寫(xiě) 神經(jīng)元網(wǎng)絡(luò) 算法
上傳時(shí)間： 2016-09-18
上傳用戶：litianchu
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(biāo)簽： development engineering cellular traffic
上傳時(shí)間： 2014-01-11
上傳用戶：Miyuki
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(biāo)簽： the relations Analysis Pattern
上傳時(shí)間： 2017-09-07
上傳用戶：SimonQQ
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(biāo)簽： Performance Evolution GPRS EDGE GSM and
上傳時(shí)間： 2020-05-27
上傳用戶：shancjb
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.
上傳時(shí)間： 2020-06-06
上傳用戶：shancjb
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(biāo)簽： Stable_adaptive_neural_network_co ntrol
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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