【書名】非線性時(shí)間序列分析 【原 書 名】 Nonlinear Time Series Analysis 【原出版社】 Cambridge University Press 【作 者】Holger Kantz,Thomas Schreiber [同作者作品] 【叢 書 名】 劍橋非線性科學(xué)系列 【出 版 社】 清華大學(xué)出版社 【書 號】 7302039062 【出版日期】 2001 年3月 【開 本】 16開 【頁 碼】 304 【版 次】1-3
標(biāo)簽： University Nonlinear Cambridge Analysis
上傳時(shí)間： 2013-12-27
上傳用戶：as275944189
Signal Processing and Linear Systems,B.P. Lathi,Berkeley-Cambridge Press book matlab codes
標(biāo)簽： Berkeley-Cambridge Processing Systems Signal
上傳時(shí)間： 2017-06-01
上傳用戶：541657925
USB接口控制器參考設(shè)計(jì),xilinx提供VHDL代碼 usb xilinx vhdl ; This program is free software; you can redistribute it and/or modify ; it under the terms of the GNU General Public License as published by ; the Free Software Foundation; either version 2 of the License, or ; (at your option) any later version. ; ; This program is distributed in the hope that it will be useful, ; but WITHOUT ANY WARRANTY; without even the implied warranty of ; MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ; GNU General Public License for more details. ; ; You should have received a copy of the GNU General Public License ; along with this program; if not, write to the Free Software ; Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
上傳時(shí)間： 2013-10-12
上傳用戶：windgate
USB接口控制器參考設(shè)計(jì),xilinx提供VHDL代碼 usb xilinx vhdl ; This program is free software; you can redistribute it and/or modify ; it under the terms of the GNU General Public License as published by ; the Free Software Foundation; either version 2 of the License, or ; (at your option) any later version. ; ; This program is distributed in the hope that it will be useful, ; but WITHOUT ANY WARRANTY; without even the implied warranty of ; MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ; GNU General Public License for more details. ; ; You should have received a copy of the GNU General Public License ; along with this program; if not, write to the Free Software ; Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
上傳時(shí)間： 2013-10-29
上傳用戶：zhouchang199
最新的支持向量機(jī)工具箱，有了它會很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.
上傳時(shí)間： 2013-12-16
上傳用戶：亞亞娟娟123
Hidden Markov Toolkit (HTK) 3.2.1 HTK is a toolkit for use in research into automatic speech recognition and has been developed by the Speech Vision Robotics Group at the Cambridge University Engineering Department (http://svr-www.eng.cam.ac.uk) and Entropic Ltd (http://www.entropic.com).
標(biāo)簽： HTK automatic research Toolkit
上傳時(shí)間： 2015-05-26
上傳用戶：myworkpost
學(xué)生信息管理系統(tǒng) GNU通用公共許可證 第二版，1991年 版權(quán)所有（C）1989，1991 Free Software foundation, Inc. 675 Mass Ave, Cambridge, MA02139, USA 允許每個(gè)人復(fù)制和發(fā)布這一許可證原始文檔的副本，但絕對不允許對它進(jìn)行任何修改。 序言 大多數(shù)軟件許可證決意剝奪你的共享和修改軟件的自由。對比之下，GNU通用公共許可證力圖保證你的共享和修改自由軟件的自由。——保證自由軟件對所有用戶是自由的。GPL適用于大多數(shù)自由軟件基金會的軟件，以及由使用這些軟件而承擔(dān)義務(wù)的作者所開發(fā)的軟件。（自由軟件基金會的其他一些軟件受GNU庫通用許可證的保護(hù)）。你也可以將它用到你的程序中。 當(dāng)我們談到自由軟件（free software）時(shí)，我們指的是自由而不是價(jià)格。我們的GNU通用公共許可證決意保證你有發(fā)布自由軟件的自由（如果你愿意，你可以對此項(xiàng)服務(wù)收取一定的費(fèi)用）；保證你能收到源程序或者在你需要時(shí)能得到它；保證你能修改軟件或?qū)⑺囊徊糠钟糜谛碌淖杂绍浖欢疫€保證你知道你能做這些事情。 為了保護(hù)你的權(quán)利，我們需要作出規(guī)定：禁止任何人不承認(rèn)你的權(quán)利，或者要求你放棄這些權(quán)利。如果你修改了自由軟件或者發(fā)布了軟件的副本，這些規(guī)定就轉(zhuǎn)化為你的責(zé)任。 例如
標(biāo)簽： GNU 信息管理系統(tǒng) 許可證
上傳時(shí)間： 2013-12-22
上傳用戶：zuozuo1215
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
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
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
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