把 sequential 有導師學習問題轉化為傳統的有導師學習問題，是weka機器學習系統（本站可下載）的一個拓展
標簽： sequential 轉化
上傳時間： 2014-11-10
上傳用戶：lanwei
文本數據庫的實現源碼,包括sequential,Index,Random 三種存取方式
標簽： sequential Random Index 數據庫
上傳時間： 2013-12-29
上傳用戶：qilin
sequential Minimal Optimization for SVM 配C++源碼
標簽： Optimization sequential Minimal SVM
上傳時間： 2014-01-12
上傳用戶：小碼農lz
ReBEL is a Matlabtoolkit of functions and scripts, designed to facilitate sequential Bayesian inference (estimation) in general state space models. This software consolidates research on new methods for recursive Bayesian estimation and Kalman filtering by Rudolph van der Merwe and Eric A. Wan. The code is developed and maintained by Rudolph van der Merwe at the OGI School of Science & Engineering at OHSU (Oregon Health & Science University).
標簽： Matlabtoolkit facilitate sequential functions
上傳時間： 2015-08-31
上傳用戶：皇族傳媒
Stack-based sequential decoder for M-QAM modulated MIMO-type problems, i.e., of fixed tree depth.
標簽： Stack-based sequential MIMO-type modulated
上傳時間： 2014-01-25
上傳用戶：515414293
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
標簽： sequential simulation posterior overview
上傳時間： 2015-12-31
上傳用戶：225588
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial and nancial problems. Although the Kalman lter is effective in the linear-Gaussian case, new methods of dealing with sequential data are required with non-standard models. Recently, there has been renewed interest in simulation-based techniques. The basic idea behind these techniques is that the current state of knowledge is encapsulated in a representative sample from the appropriate posterior distribution. As time goes on, the sample evolves and adapts recursively in accordance with newly acquired data. We give a critical review of recent developments, by reference to oil well monitoring, ion channel monitoring and tracking problems, and propose some alternative algorithms that avoid the weaknesses of the current methods.
標簽： monitoring sequential industria accurate
上傳時間： 2013-12-17
上傳用戶：familiarsmile
Particle Filtering for sequential Spacecraft Attitude Estimation 衛星姿態估計的另外一種粒子濾波的介紹
標簽： Estimation sequential Spacecraft Filtering
上傳時間： 2014-07-06
上傳用戶：cxl274287265
自己編的matlab程序。用于模式識別中特征的提取。是特征提取中的sequential Forward Selection方法，簡稱sfs.它可以結合Maximum-Likelihood-Classifier分類器進行使用。
標簽： sequential Selection Forward matlab
上傳時間： 2016-04-02
上傳用戶：ma1301115706
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
