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Improved sparse least-squares support vector machines

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i; j ∈S i=1 j ∈S
Setting the partial derivatives with respect to ÿ and b to zero, and dividing through by 2 =‘, yields
‘ ‘
ÿi
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kij + ‘b =
j =1
yj
and
ÿi
School of Information Systems, University of East Anglia, Norwich NR4 7TJ, UK Received 4 September 2001; received in revised form 4 March 2002; accepted 10 March 2002
2 ‘
+
‘
(yi − w · (xi ) − b)2 ;
(3)
i=1
The optimal expansion can be found by solving the system of linear equations (2) substituting = (K + ‘ −1 I ).
G.C. Cawley, N.L.C. Talbot / Neurocomputing 48 (2002) 1025 – 1031
∗
This work was supported by Royal Society Research Grant RSRG-22270. Corresponding author. E-mail address: gcc@ (G.C. Cawley).
0925-2312/02/$ - see front matter c 2002 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 2 3 1 2 ( 0 2 ) 0 0 6 0 6 - 9
Keywords: Support vector machines; Sparse approximation; Kernel ridge regression
1. Least-squares support vector machines The least-squares support vector machine (LS-SVM), given training data, D = d {xi ; yi }‘ i=1 ; x ∈ X ⊂ R ; yi ∈ Y ⊂ R, constructs a linear regression model, f (x) = w · (x) + b, in a high-dimensional feature space, F( : X → F), induced by a kernel function deÿning the inner product K(x; x ) = (x) · (x ). A commonly used kernel is the Gaussian radial basis function, K(x; x ) = exp{− −2 x − x 2 }. The optimal values for the weight vector, w, and bias, b, are given by the minimum of an
1027
The generalisation performance of sparse approximations of least-squares support vector machines can be further improved by including the residuals of patterns not used in the kernel expansion within the objective function. The weight vector, w, is then represented as a weighted sum of selected training patterns, w = i∈S ÿi (xi ), where S ⊂ {1; 2; : : : ; ‘} is the set of indices of training patterns used to form the kernel expansion. The objective function (3) can then be written as 2 ‘ 1 yi − W (ÿ; b) = ÿi ÿj kij + ÿj kij − b : 2 ‘
Neurocomputing 48 (2002) 1025 – 1031
/locate/neucom
Letters
Improved sparse least-squares support vector machines
Gavin C. Cawley ∗ , Nicola L.C. Talbot
1026
G.C. Cawley, N.L.C. Talbot / Neurocomputing 48 (2002) 1025 – 1031
objective function W (w; b) = 1 w 2
2 ‘
+
i=1
(yi − w · (xi ) − b)2 ;
(1)
implementing a quadratic regularisation of a sum-of-squares empirical risk. The representer theorem [3] states that the solution of this problem can be written as an expansion in terms of training patterns,
Abstract Suykens et al. (Neurocomputing (2002), in press) describe a weighted least-squares formulation of the support vector machine for regression problems and present a simple algorithm for sparse approximation of the typically fully dense kernel expansions obtained using this method. In this paper, we present an improved method for achieving sparsity in least-squares support vector machines, which takes into account the residuals for all training patterns, rather than only those incorporated in the sparse kernel expansion. The superiority of this algorithm is demonstrated on the motorcycle and Boston housing data sets. c 2002 Elsevier Science B.V. All rights reserved.
k =1
where
= {!ij }i; j∈S , !ij = (‘= 2 )kij +
i∈S
‘ kir + 2
‘ j =1
kjr kji + b
‘
‘
kir =
i=1 i=1
Hale Waihona Puke yi kir ;∀r ∈ S:
These equations can be expressed as a system of |S| + 1 linear equations in |S| + 1 unknowns, c ÿ ; = ‘ T b ‘ yk
2. Improved sparsiÿcation In this section, we present two modiÿcations to the least-squares support vector machine. The ÿrst modiÿcation is based on the observation that changing the number of training patterns alters the balance between the sum-of-square empirical risk and the quadratic regulariser in the objective function (1). This implies that model selection based on cross-validation schemes will generally lead to under-regularised models. This can easily be remedied by quadratic regularisation of a mean-squared-error empirical risk, W (w; b) = 1 w 2
T
0
b
=
y 0
;
(2)
T T where = (K + −1 I ), K = {kij = K(xi ; xj )}‘ i; j =1 , 1 = (1; 1; : : : ; 1) , y = (yi ; y2 ; : : : ; y‘ ) and = ( i ; 2 ; : : : ; ‘ )T . The kernel expansions describing least-squares support vector machines are typically fully dense, i.e. i = 0; ∀i ∈ {1; 2; : : : ; ‘}. Suykens et al. [6,7] advocate the use of the following algorithm to obtain a sparse approximation: A LS-SVM is trained on the entire data set, yielding a vector of coe cients, . A small fraction of the data (say 5%), associated with coe cients having the smallest magnitudes, are discarded and the LS-SVM retrained on the remaining data. This process is repeated until a su ciently small kernel expansion is obtained. It is observed in [7] that model selection should be performed at each iteration to ÿnd new values for the regularisation parameter, and any kernel parameters, such that optimal generalisation is achieved.

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基于 SIFT 的宽基线立体影像密集匹配

基于 SIFT 的宽基线立体影像密集匹配

第40卷 第5期测 绘 学 报Vol.40,No.5 2011年10月Acta Geoda etica et Ca rtographica SinicaOct.,2011文章编号:1001-1595(2011)05-0537-07基于SIFT 的宽基线立体影像密集匹配杨化超1,2,姚国标2,王永波1,21.国土环境与灾害监测国家测绘局重点实验室,江苏徐州221116;2.中国矿业大学环境与测绘学院,江苏徐州221116D ense M a tching for W ide Base -line Stereo Im ages Based on SIFTYANG H uacha o 1,2,YAO Guobiao 2,WANG Yongbo 1,21.Key La bora tory for La nd Envi ronment &Disa ster M onitoring of SB SM ,Xuzh ou 221116,China;2.School of En v ironmenta l &Spa tia l Informa tics,China University of M ining &Technology,Xuzhou 221116,Ch i naAbstract :A novel mul t-i stage quas-i dense matchi ng al gorithm for w i de base -l ine stereo i m a ges i s introduced ba sed on SIFT a nd dual constra ints of epipola r geom etry and homographic mapping.The proposed a lgori thm i ncludes foll ow i ng three sta ges: The opti m a l SIFT features w it h good spa tial dist ribution a nd l arge informat ion content are fi rst selected,and matched by usi ng the least squares ma tching m ethod,t hen the fundamenta l and homographic m a trix can be esti mated by usi ng these i ni tia l sparse correspondences with hi gher precisi on; For the other SIFT features,the affine tra nsformati on para meters betw een mat chi ng window s a re i tera tivel y optimi zed by using the slope a ngl e of cor respondent epipola r lines and scale i nformati on of SIFT feat ures,a nd a ffi ne invaria nt feature descri ptors are extra cted from the corrected matchi ng window s,then correspondences can be determined by Eucli d dista nce and dua l const raint i nformati on; Considering the lower repeata bil ity ra te of fea ture detection for wide base -li ne stereo i mages,for the unma tched poi nt s ext racted from left and ri ght ima ges of stereo pa irs,mat ching ca n be car ried out by adopting t w o -w a y search strat egy from left to right i mage or from right t o left i mage ba sed on the rapi d SSD simi lari ty cost functi on and affine recti fi ed dua l constrai nts region,a nd t he least squares curve sur fa ce fitti ng wei ghted by Ga ussi an -distance algori thm is adopted to i m prove the preci sion of matchi ng results.Test resul ts usi ng pra cti ca l w i de ba se -l ine i mage pa irs i ndi ca te the proposed al gori thm is effective a nd can provide rel ia ble dense or qua s -i dense ma tching points for subsequent 3D reconst ruction.Key words :sca le invaria nt feature transform a tion;quas-i dense matchi ng;affine i nva riant;homogra phi c mappi ng 摘 要:提出基于对极几何和单应映射双重约束及SIFT 特征的宽基线立体影像多阶段准密集匹配算法。

基于子空间字典偶学习的高光谱图像分类

基于子空间字典偶学习的高光谱图像分类

基于子空间字典偶学习的高光谱图像分类王金甲;姜雪;杨中玉【摘要】针对高光谱高分辨率带来巨大数据量和空间分辨率引起混合像元的问题,提出了基于子空间(subspace)的字典偶学习(DPL)算法,简称DPLsub算法.DPL算法是对字典学习的改进,它通过学习得到综合字典和分析字典,在模式识别中体现了高效性,而子空间投影的方法能更好地表征噪声和高度混合的像元.将光谱和空间特征融合的方法用于分类研究试验.实验数据是两幅高光谱影像,比较了子空间字典偶学习(DPLsub)模型和其他三种分类器即最小二乘支持向量机(LS-SVM)、稀疏多分类回归(SMLR)和字典学习(DL-OMP)的分类结果.实验结果显示,DPLsub算法无论在时间上还是精度上都优于其他算法,证明了这种子空间字典偶学习方法对高光谱图像分类的可行性与高效性.【期刊名称】《高技术通讯》【年(卷),期】2016(026)005【总页数】8页(P483-490)【关键词】高光谱图像分类;子空间投影;混合像元;字典偶学习(DPL);多特征融合【作者】王金甲;姜雪;杨中玉【作者单位】燕山大学信息科学与工程学院秦皇岛066004;燕山大学信息科学与工程学院秦皇岛066004;燕山大学信息科学与工程学院秦皇岛066004【正文语种】中文随着传感器与成像技术的发展，遥感影像在军事、农业等方面发挥的重要作用日益明显。

但由于高光谱数据具有上百个通道，数据量非常大，存在大量的混合像元和数据冗余现象，给图像传输和存储带来了困难，另外，由于波段间强的相关性和类内多样性的存在使高光谱图像有很多特征提取方面的困难和分类效果不理想等问题，针对这些具体问题研究学者们已提出了很多解决方法[1]，例如高空间高分辨率的遥感图像分类方法，其主要特点是先降维再提特征实现分类研究[2，3]。

同时在现实中，由于种种原因，在像元对应的瞬间视角内包含多于一种地物，因此形成了混合像元，早在2008年罗文斐等人就提出了基于子空间距离解混高光谱图像的算法[4]，之后更多关于子空间投影的方法应用到图像分类[5，6]。

机械故障稀疏特征相似性度量优化研究

机械故障稀疏特征相似性度量优化研究

Vol. 38 No. 2Mar. 2020第38卷第2期2020年3月吉林大学学报(信息科学版)Journal of Jilin University ( Information Science Edition )文章编号：1671 -5896 (2020 )02-0154-06机械故障稀疏特征相似性度量优化研究徐世福，蒋亚南(宁波大学科学技术学院，浙江宁波315212)摘要：针对当前机械故障诊断研究忽略了对其参数的选取与优化，导致准确性较差等问题，提出基于量子遗传 算法优化的机械故障稀疏特征相似性度量方法。

基于先进行信号非线性混合，再进行去混合。

将峭度作为目 标函数.利用量子遗传算法，对盲源分离过程的分离矩阵参数与非线性去混合参数进行优化，实现机械故障盲 源分离' 基于故障信号处理，利用量子遗传算法与最小二乘支持向量机(LSSVM ： Least Squares Support Vector Machine)相结合实现机械故障稀疏特征相似性度量。

当LSSVM 在机械故障诊断时对模型参数选取，利用量子遗 传算法针对LSSVM 模型参数进行优化。

将LSSVM 参数选取问题转换为优化问题，利用优化后的LSSVM 分类 模型实现机械故障稀疏特征相似模式分类。

实验结果表明，该方法可以实现高效盲源分离.机械故障诊断准确 率高，运行性能良好。

关键词：量子遗传算法；机械故障；特征；相似性度量中图分类号：TP165 文献标识码：AResearch on Similarity Measure of Sparse Feature of Mechanical FaultBased on Quantum Genetic Algorithm OptimizationXU Shifu, JIANG Yanan(College of Science and Technology , Ningbo University , Ningbo 315212 , China)Abstract : Aiming at the problem that the selection and optimization of mechanical fault parameters are neglected in the current research on mechanical fault diagnosis, which leads to poor accuracy , this paper proposes a sparse feature similarity measurement method for mechanical fault based on quantum genetic algorithm optimization. According to the first signal nonlinear mixing, then demixing. With kurtosis as the objective function and quantum genetic algorithm , the separation matrix parameters and nonlinear de-mixing parameters of blind source separation are optimized to realize blind source separation of mechanical faults. Based on fault signal processing , the similarity measurement of mechanical fault sparse features is realized by combining quantum genetic algorithm with LSSVM ( Least Squares Support Vector Machine ). LSSVM selects the model parameters during the mechanical fault diagnosis , the quantum genetic algorithm is used to optimize the model parameters of LSSVM. The parameter selection problem of LSSVM is transformed to an optimization problem , and the optimized LSSVM classification model is used to realize the classification of mechanical fault sparse feature similarity mode. The experimental results show that the method can achieve high efficiency blind source separation , high accuracy in mechanical fault diagnosis and good performance.Key words : quantum genetic algorithm ； mechanical failure ； feature ； similarity measure收稿日期：2019-11-20基金项目：浙江省教育厅基金资助项目(Y201737089)作者简介：徐世福(1985—),男，山东临沂人，宁波大学助理研究员，主要从事机构创新与自动化应用研究，(Tel )86-152********(E-mail ) xushifu369@ 163. com 0第2期徐世福，等：机械故障稀疏特征相似性度量优化研究1550引言在机械运行过程中，常有各种类型故障发生机械故障问题复杂且多变，而盲源机械的运行故障会导致机器损坏，致使生产过程中断，甚至发生安全事故。

matlab工具箱安装教程

matlab工具箱安装教程

1.1 如果是Matlab安装光盘上的工具箱，重新执行安装程序，选中即可；1.2 如果是单独下载的工具箱，一般情况下仅需要把新的工具箱解压到某个目录。

2 在matlab的file下面的set path把它加上。

3 把路径加进去后在file→Preferences→General的Toolbox Path Caching里点击update Toolbox Path Cache更新一下。

4 用which newtoolbox_command.m来检验是否可以访问。

如果能够显示新设置的路径，则表明该工具箱可以使用了。

把你的工具箱文件夹放到安装目录中“toolbox”文件夹中，然后单击“file”菜单中的“setpath”命令，打开“setpath”对话框，单击左边的“ADDFolder”命令，然后选择你的那个文件夹，最后单击“SAVE”命令就OK了。

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Taxhttp://www-ict.ewi.tudelft.nl/~davidt/dd_tools/dd_manual.htmlMBEhttp://www.pmarneffei.hku.hk/mbetoolbox/Betabolic network toolbox for Matlabhttp://www.molgen.mpg.de/~lieberme/pages/network_matlab.htmlPharmacokinetics toolbox for Matlabhttp://page.inf.fu-berlin.de/~lieber/seiten/pbpk_toolbox.htmlThe SpiderThe spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be compared with, e.g model selection, statistical tests and visual plots. 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MALTAB astronomy/~abrecht/Matlab-codes/AudioMA Toolbox for Matlab Implementing Similarity Measures for Audiohttp://www.oefai.at/~elias/ma/index.htmlMAD - Matlab Auditory Demonstrations/~martin/MAD/docs/mad.htmMusic Analysis - Toolbox for Matlab : Feature Extraction from Raw Audio Signals for Content-Based Music Retrihttp://www.ai.univie.ac.at/~elias/ma/WarpTB - Matlab Toolbox for Warped DSPBy Aki Härmä and Matti Karjalainenhttp://www.acoustics.hut.fi/software/warp/MATLAB-related Softwarehttp://www.dpmi.tu-graz.ac.at/~schloegl/matlab/Biomedical Signal data formats (EEG machine specific file formats with Matlab import routines)http://www.dpmi.tu-graz.ac.at/~schloegl/matlab/eeg/MPEG Encoding library for MATLAB Movies (Created by David Foti)It enables MATLAB users to read (MPGREAD) or write (MPGWRITE) MPEG movies. That should help Video Quality project.Filter Design packagehttp://www.ee.ryerson.ca:8080/~mzeytin/dfp/index.htmlOctave by Christophe COUVREUR (Generates normalized A-weigthing, C-weighting, octave and one-third-octave digital filters)/matlabcentral/fileexchange/loadFile.do?objectType=file&object Id=69Source Coding MATLAB Toolbox/users/kieffer/programs.htmlBio Medical Informatics (Top)CGH-Plotter: MATLAB Toolbox for CGH-data AnalysisCode: http://sigwww.cs.tut.fi/TICSP/CGH-Plotter/Poster: http://sigwww.cs.tut.fi/TICSP/CSB2003/Posteri_CGH_Plotter.pdfThe Brain Imaging Software Toolboxhttp://www.bic.mni.mcgill.ca/software/MRI Brain Segmentation/matlabcentral/fileexchange/loadFile.do?objectId=4879Chemometrics (providing PCA) (Top)Matlab Molecular Biology & Evolution Toolbox(Toolbox Enables Evolutionary Biologists to Analyze and View DNA and Protein Sequences) James J. 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P.)http://webpages.ull.es/users/sympbst/pag_ing/pag_metmap/index.htmDoseLab - A set of software programs for quantitative comparison of measured and computed radiation dose distributions/GenBank Overview/Genbank/GenbankOverview.htmlMatlab: /matlabcentral/fileexchange/loadFile.do?objectId=1139CodingCode for the estimation of Scaling Exponentshttp://www.cubinlab.ee.mu.oz.au/~darryl/secondorder_code.htmlControl (Top)Control Tutorial for Matlab/group/ctm/AnotherCommunications (Top)Channel Learning Architecture toolbox(This Matlab toolbox is a supplement to the article "HiperLearn: A High Performance Learning Architecture")http://www.isy.liu.se/cvl/Projects/hiperlearn/Source Coding MATLAB Toolbox/users/kieffer/programs.htmlTCP/UDP/IP Toolbox 2.0.4/matlabcentral/fileexchange/loadFile.do?objectId=345&objectT ype=fileHome Networking Basis: Transmission Environments and Wired/Wireless Protocols Walter Y. Chen/support/books/book5295.jsp?category=new&language=-1MATLAB M-files and Simulink models/matlabcentral/fileexchange/loadFile.do?objectId=3834&object Type=file•OPNML/MATLAB Facilities/OPNML_Matlab/Mesh Generation/home/vavasis/qmg-home.htmlOpenFEM : An Open-Source Finite Element Toolbox/CALFEM is an interactive computer program for teaching the finite element method (FEM)http://www.byggmek.lth.se/Calfem/frinfo.htmThe Engineering Vibration Toolbox/people/faculty/jslater/vtoolbox/vtoolbox.htmlSaGA - Spatial and Geometric Analysis Toolboxby Kirill K. Pankratov/~glenn/kirill/saga.htmlMexCDF and NetCDF Toolbox For Matlab-5&6/staffpages/cdenham/public_html/MexCDF/nc4ml5.htmlCUEDSID: Cambridge University System Identification Toolbox/jmm/cuedsid/Kriging Toolbox/software/Geostats_software/MATLAB_KRIGING_TOOLBOX.htmMonte Carlo (Dr Nando)http://www.cs.ubc.ca/~nando/software.htmlRIOTS - The Most Powerful Optimal Control Problem Solver/~adam/RIOTS/ExcelMATLAB xlsheets/matlabcentral/fileexchange/loadFile.do?objectId=4474&objectTy pe=filewrite2excel/matlabcentral/fileexchange/loadFile.do?objectId=4414&objectTy pe=fileFinite Element Modeling (FEM) (Top)OpenFEM - An Open-Source Finite Element Toolbox/NLFET - nonlinear finite element toolbox for MATLAB ( framework for setting up, solving, and interpreting results for nonlinear static and dynamic finite element analysis.)/GetFEM - C++ library for finite element methods elementary computations with a Matlabinterfacehttp://www.gmm.insa-tlse.fr/getfem/FELIPE - FEA package to view results ( contains neat interface to MATLA/~blstmbr/felipe/Finance (Top)A NEW MATLAB-BASED TOOLBOX FOR COMPUTER AIDED DYNAMIC TECHNICAL TRADINGStephanos Papadamou and George StephanidesDepartment of Applied Informatics, University Of Macedonia Economic & Social Sciences, Thessaloniki, Greece/fen31/one_time_articles/dynamic_tech_trade_matlab6.htm Paper: :8089/eps/prog/papers/0201/0201001.pdfCompEcon Toolbox for Matlab/~pfackler/compecon/toolbox.htmlGenetic Algorithms (Top)The Genetic Algorithm Optimization Toolbox (GAOT) for Matlab 5/mirage/GAToolBox/gaot/Genetic Algorithm ToolboxWritten & distributed by Andy Chipperfield (Sheffield University, UK)/uni/projects/gaipp/gatbx.htmlManual: /~gaipp/ga-toolbox/manual.pdfGenetic and Evolutionary Algorithm Toolbox (GEATbx)/Evolutionary Algorithms for MATLAB/links/ea_matlab.htmlGenetic/Evolutionary Algorithms for MATLABhttp://www.systemtechnik.tu-ilmenau.de/~pohlheim/EA_Matlab/ea_matlab.html GraphicsVideoToolbox (C routines for visual psychophysics on Macs by Denis Pelli)/VideoToolbox/Paper: /pelli/pubs/pelli1997videotoolbox.pdf4D toolbox/~daniel/links/matlab/4DToolbox.htmlImages (Top)Eyelink Toolbox/eyelinktoolbox/Paper: /eyelinktoolbox/EyelinkToolbox.pdfCellStats: Automated statistical analysis of color-stained cell images in Matlabhttp://sigwww.cs.tut.fi/TICSP/CellStats/SDC Morphology Toolbox for MATLAB (powerful collection of latest state-of-the-art gray-scale morphological tools that can be applied to image segmentation, non-linear filtering, pattern recognition and image analysis)/Image Acquisition Toolbox/products/imaq/Halftoning Toolbox for MATLAB/~bevans/projects/halftoning/toolbox/index.htmlDIPimage - A Scientific Image Processing Toolbox for MATLABhttp://www.ph.tn.tudelft.nl/DIPlib/dipimage_1.htmlPNM Toolboxhttp://home.online.no/~pjacklam/matlab/software/pnm/index.htmlAnotherICA / KICA and KPCA (Top)ICA TU Toolboxhttp://mole.imm.dtu.dk/toolbox/menu.htmlMISEP Linear and Nonlinear ICA Toolboxhttp://neural.inesc-id.pt/~lba/ica/mitoolbox.htmlKernel Independant Component Analysis/~fbach/kernel-ica/index.htmMatlab: kernel-ica version 1.2KPCA- Please check the software section of kernel machines.KernelStatistical Pattern Recognition Toolboxhttp://cmp.felk.cvut.cz/~xfrancv/stprtool/MATLABArsenal A MATLAB Wrapper for Classification/tmp/MATLABArsenal.htmMarkov (Top)MapHMMBOX 1.1 - Matlab toolbox for Hidden Markov Modelling using Max. Aposteriori EM Prerequisites: Matlab 5.0, Netlab. Last Updated: 18 March 2002./~parg/software/maphmmbox_1_1.tarHMMBOX 4.1 - Matlab toolbox for Hidden Markov Modelling using Variational Bayes Prerequisites: Matlab 5.0,Netlab. Last Updated: 15 February 2002../~parg/software/hmmbox_3_2.tar/~parg/software/hmmbox_4_1.tarMarkov Decision Process (MDP) Toolbox for MatlabKevin Murphy, 1999/~murphyk/Software/MDP/MDP.zipMarkov Decision Process (MDP) Toolbox v1.0 for MATLABhttp://www.inra.fr/bia/T/MDPtoolbox/Hidden Markov Model (HMM) Toolbox for Matlab/~murphyk/Software/HMM/hmm.htmlBayes Net Toolbox for Matlab/~murphyk/Software/BNT/bnt.htmlMedical (Top)EEGLAB Open Source Matlab Toolbox for Physiological Research (formerly ICA/EEG Matlabtoolbox)/~scott/ica.htmlMATLAB Biomedical Signal Processing Toolbox/Toolbox/Powerful package for neurophysiological data analysis ( Igor Kagan webpage)/Matlab/Unitret.htmlEEG / MRI Matlab Toolbox/Microarray data analysis toolbox (MDAT): for normalization, adjustment and analysis of gene expression_r data.Knowlton N, Dozmorov IM, Centola M. Department of Arthritis and Immunology, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA 73104. We introduce a novel Matlab toolbox for microarray data analysis. This toolbox uses normalization based upon a normally distributed background and differential gene expression_r based on 5 statistical measures. The objects in this toolbox are open source and can be implemented to suit your application. AVAILABILITY: MDAT v1.0 is a Matlab toolbox and requires Matlab to run. MDAT is freely available at:/publications/2004/knowlton/MDAT.zipMIDI (Top)MIDI Toolbox version 1.0 (GNU General Public License)http://www.jyu.fi/musica/miditoolbox/Misc. (Top)MATLAB-The Graphing Tool/~abrecht/matlab.html3-D Circuits The Circuit Animation Toolbox for MATLAB/other/3Dcircuits/SendMailhttp://carol.wins.uva.nl/~portegie/matlab/sendmail/Coolplothttp://www.reimeika.ca/marco/matlab/coolplots.htmlMPI (Matlab Parallel Interface)Cornell Multitask Toolbox for MATLAB/Services/Software/CMTM/Beolab Toolbox for v6.5Thomas Abrahamsson (Professor, Chalmers University of Technology, Applied Mechanics,Göteborg, Sweden)http://www.mathworks.nl/matlabcentral/fileexchange/loadFile.do?objectId=1216&objectType =filePARMATLABNeural Networks (Top)SOM Toolboxhttp://www.cis.hut.fi/projects/somtoolbox/Bayes Net Toolbox for Matlab/~murphyk/Software/BNT/bnt.htmlNetLab/netlab/Random Neural Networks/~ahossam/rnnsimv2/ftp: ftp:///pub/contrib/v5/nnet/rnnsimv2/NNSYSID Toolbox (tools for neural network based identification of nonlinear dynamic systems) http://www.iau.dtu.dk/research/control/nnsysid.htmlOceanography (Top)WAFO. Wave Analysis for Fatigue and Oceanographyhttp://www.maths.lth.se/matstat/wafo/ADCP toolbox for MATLAB (USGS, USA)Presented at the Hydroacoustics Workshop in Tampa and at ADCP's in Action in San Diego /operations/stg/pubs/ADCPtoolsSEA-MAT - Matlab Tools for Oceanographic AnalysisA collaborative effort to organize and distribute Matlab tools for the Oceanographic Community /Ocean Toolboxhttp://www.mar.dfo-mpo.gc.ca/science/ocean/epsonde/programming.htmlEUGENE D. GALLAGHER(Associate Professor, Environmental, Coastal & Ocean Sciences)/edgwebp.htmOptimization (Top)MODCONS - a MATLAB Toolbox for Multi-Objective Control System Design/mecheng/jfw/modcons.htmlLazy Learning Packagehttp://iridia.ulb.ac.be/~lazy/SDPT3 version 3.02 -- a MATLAB software for semidefinite-quadratic-linear programming .sg/~mattohkc/sdpt3.htmlMinimum Enclosing Balls: Matlab Code/meb/SOSTOOLS Sum of Squares Optimi zation Toolbox for MATLAB User’s guide/sostools/sostools.pdfPSOt - a Particle Swarm Optimization Toolbox for use with MatlabBy Brian Birge ... A Particle Swarm Optimization Toolbox (PSOt) for use with the Matlab scientific programming environment has been developed. PSO isintroduced briefly and then the use of the toolbox is explained with some examples. A link to downloadable code is provided.Plot/software/plotting/gbplot/Signal Processing (Top)Filter Design with Motorola DSP56Khttp://www.ee.ryerson.ca:8080/~mzeytin/dfp/index.htmlChange Detection and Adaptive Filtering Toolboxhttp://www.sigmoid.se/Signal Processing Toolbox/products/signal/ICA TU Toolboxhttp://mole.imm.dtu.dk/toolbox/menu.htmlTime-Frequency Toolbox for Matlabhttp://crttsn.univ-nantes.fr/~auger/tftb.htmlVoiceBox - Speech Processing Toolbox/hp/staff/dmb/voicebox/voicebox.htmlLeast Squared - Support Vector Machines (LS-SVM)http://www.esat.kuleuven.ac.be/sista/lssvmlab/WaveLab802 : the Wavelet ToolboxBy David Donoho, Mark Reynold Duncan, Xiaoming Huo, Ofer Levi /~wavelab/Time-series Matlab scriptshttp://wise-obs.tau.ac.il/~eran/MATLAB/TimeseriesCon.htmlUvi_Wave Wavelet Toolbox Home Pagehttp://www.gts.tsc.uvigo.es/~wavelets/index.htmlAnotherSupport Vector Machine (Top)MATLAB Support Vector Machine ToolboxDr Gavin CawleySchool of Information Systems, University of East Anglia/~gcc/svm/toolbox/LS-SVM - SISTASVM toolboxes/dmi/svm/LSVM Lagrangian Support Vector Machine/dmi/lsvm/Statistics (Top)Logistic regression/SAGA/software/saga/Multi-Parametric Toolbox (MPT) A tool (not only) for multi-parametric optimization. http://control.ee.ethz.ch/~mpt/ARfit: A Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive modelshttp://www.mat.univie.ac.at/~neum/software/arfit/The Dimensional Analysis Toolbox for MATLABHome: http://www.sbrs.de/Paper: http://www.isd.uni-stuttgart.de/~brueckner/Papers/similarity2002.pdfFATHOM for Matlab/personal/djones/PLS-toolbox/Multivariate analysis toolbox (N-way Toolbox - paper)http://www.models.kvl.dk/source/nwaytoolbox/index.aspClassification Toolbox for Matlabhttp://tiger.technion.ac.il/~eladyt/classification/index.htmMatlab toolbox for Robust Calibrationhttp://www.wis.kuleuven.ac.be/stat/robust/toolbox.htmlStatistical Parametric Mapping/spm/spm2.htmlEVIM: A Software Package for Extreme Value Analysis in Matlabby Ramazan Gençay, Faruk Selcuk and Abdurrahman Ulugulyagci, 2001.Manual (pdf file) evim.pdf - Software (zip file) evim.zipTime Series Analysishttp://www.dpmi.tu-graz.ac.at/~schloegl/matlab/tsa/Bayes Net Toolbox for MatlabWritten by Kevin Murphy/~murphyk/Software/BNT/bnt.htmlOther: /information/toolboxes.htmlARfit: A Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models/~tapio/arfit/M-Fithttp://www.ill.fr/tas/matlab/doc/mfit4/mfit.htmlDimensional Analysis Toolbox for Matlab/The NaN-toolbox: A statistic-toolbox for Octave and Matlab®... handles data with and without MISSING VALUES.http://www-dpmi.tu-graz.ac.at/~schloegl/matlab/NaN/Iterative Methods for Optimization: Matlab Codes/~ctk/matlab_darts.htmlMultiscale Shape Analysis (MSA) Matlab Toolbox 2000p.br/~cesar/projects/multiscale/Multivariate Ecological & Oceanographic Data Analysis (FATHOM)From David Jones/personal/djones/glmlab (Generalized Linear Models in MATLA.au/staff/dunn/glmlab/glmlab.htmlSpacial and Geometric Analysis (SaGA) toolboxInteresting audio links with FAQ, VC++, on the topic机器学习网站北京大学视觉与听觉信息处理实验室北京邮电大学模式识别与智能系统学科复旦大学智能信息处理开放实验室IEEE Computer Society北京映象站点计算机科学论坛机器人足球赛模式识别国家重点实验室南京航空航天大学模式识别与神经计算实验室- PARNEC南京大学机器学习与数据挖掘研究所- LAMDA南京大学人工智能实验室南京大学软件新技术国家重点实验室人工生命之园数据挖掘研究院微软亚洲研究院中国科技大学人工智能中心中科院计算所中科院计算所生物信息学实验室中科院软件所中科院自动化所中科院自动化所人工智能实验室ACL Special Interest Group on Natural Language Learning (SIGNLL)ACMACM Digital LibraryACM SIGARTACM SIGIRACM SIGKDDACM SIGMODAdaptive Computation Group at University of New MexicoAI at Johns HopkinsAI BibliographiesAI Topics: A dynamic online library of introductory information about artificial intelligence Ant Colony OptimizationARIES Laboratory: Advanced Research in Intelligent Educational SystemsArtificial Intelligence Research in Environmental Sciences (AIRIES)Austrian Research Institute for AI (OFAI)Back Issues of Neuron DigestBibFinder: a computer science bibliography search engine integrating many other engines BioAPI ConsortiumBiological and Computational Learning Center at MITBiometrics ConsortiumBoosting siteBrain-Style Information Systems Research Group at RIKEN Brain Science Institute, Japan British Computer Society Specialist Group on Expert SystemsCanadian Society for Computational Studies of Intelligence (CSCSI)CI Collection of BibTex DatabasesCITE, the first-stop source for computational intelligence information and services on the web Classification Society of North AmericaCMU Advanced Multimedia Processing GroupCMU Web->KB ProjectCognitive and Neural Systems Department of Boston UniversityCognitive Sciences Eprint Archive (CogPrints)COLT: Computational Learning TheoryComputational Neural Engineering Laboratory at the University of FloridaComputational Neurobiology Lab at California, USAComputer Science Department of National University of SingaporeData Mining Server Online held by Rudjer Boskovic InstituteDatabase Group at Simon Frazer University, CanadaDBLP: Computer Science BibliographyDigital Biology: about creating artificial lifeDistributed AI Unit at Queen Mary & Westfield College, University of LondonDistributed Artificial Intelligence at HUJIDSI Neural Networks group at the Université di Firenze, ItalyEA-related literature at the EvALife research group at DAIMI, University of Aarhus, Denmark Electronic Research Group at Aberdeen UniversityElsevierComputerScienceEuropean Coordinating Committee for Artificial Intelligence (ECCAI)European Network of Excellence in ML (MLnet)European Neural Network Society (ENNS)Evolutionary Computing Group at University of the West of EnglandEvolutionary Multi-Objective Optimization RepositoryExplanation-Based Learning at University of Illinoise at Urbana-ChampaignFace Detection HomepageFace Recognition Vendor TestFace Recognition HomepageFace Recognition Research CommunityFingerpassftp of Jude Shavlik's Machine Learning Group (University of Wisconsin-Madison)GA-List Searchable DatabaseGenetic Algorithms Digest ArchiveGenetic Programming BibliographyGesture Recognition HomepageHCI Bibliography Project contain extended bibliographic information (abstract, key words, table of contents, section headings) for most publications Human-Computer Interaction dating back to 1980 and selected publications before 1980IBM ResearchIEEEIEEE Computer SocietyIEEE Neural Networks SocietyIllinois Genetic Algorithms Laboratory (IlliGAL)ILP Network of ExcellenceInductive Learning at University of Illinoise at Urbana-ChampaignIntelligent Agents RepositoryIntellimedia Project at North Carolina State UniversityInteractive Artificial Intelligence ResourcesInternational Association of Pattern RecognitionInternational Biometric Industry AssociationInternational Joint Conference on Artificial Intelligence (IJCAI)International Machine Learning Society (IMLS)International Neural Network Society (INNS)Internet Softbot Research at University of WashingtonJapanese Neural Network Society (JNNS)Java Agents for Meta-Learning Group (JAM) at Computer Science Department, Columbia University, for Fraud and Intrusion Detection Using Meta-Learning AgentsKernel MachinesKnowledge Discovery MineLaboratory for Natural and Simulated Cognition at McGill University, CanadaLearning Laboratory at Carnegie Mellon UniversityLearning Robots Laboratory at Carnegie Mellon UniversityLaboratoire d'Informatique et d'Intelligence Artificielle (IIA-ENSAIS)Machine Learning Group of Sydney University, AustraliaMammographic Image Analysis SocietyMDL Research on the WebMirek's Cellebration: 1D and 2D Cellular Automata explorerMIT Artificial Intelligence LaboratoryMIT Media LaboratoryMIT Media Laboratory Vision and Modeling GroupMLNET: a European network of excellence in Machine Learning, Case-based Reasoning and Knowledge AcquisitionMLnet Machine Learning Archive at GMD includes papers, software, and data sets MIRALab at University of Geneva: leading research on virtual human simulationNeural Adaptive Control Technology (NACT)Neural Computing Research Group at Aston University, UKNeural Information Processing Group at Technical University of BerlinNIPSNIPS OnlineNeural Network Benchmarks, Technical Reports,and Source Code maintained by Scott Fahlman at CMU; source code includes Quickprop, Cascade-Correlation, Aspirin/Migraines Neural Networks FAQ by Lutz PrecheltNeural Networks FAQ by Warren S. SarleNeural Networks: Freeware and Shareware ToolsNeural Network Group at Department of Medical Physics and Biophysics, University ofNeural Network Group at Université Catholique de LouvainNeural Network Group at Eindhoven University of TechnologyNeural Network Hyperplane Animator program that allows easy visualization of training data and weights in a back-propagation neural networkNeural Networks Research at TUT/ELENeural Networks Research Centre at Helsinki University of Technology, FinlandNeural Network Speech Group at Carnegie Mellon UniversityNeural Text Classification with Neural NetworksNonlinearity and Complexity HomepageOFAI and IMKAI library information system, provided by the Department of Medical Cybernetics and Artificial Intelligence at the University of Vienna (IMKAI) and the Austrian Research Institute for Artificial Intelligence (OFAI). It contains over 36,000 items (books, research papers, conference papers, journal articles) from many subareas of AI OntoWeb: Ontology-based information exchange for knowledge management and electronic commercePortal on Neural Network ForecastingPRAG: Pattern Recognition and Application Group at University of CagliariQuest Project at IBM Almaden Research Center: an academic website focusing on classification and regression trees. Maintained by Tjen-Sien LimReinforcement Learning at Carnegie Mellon UniversityResearchIndex: NECI Scientific Literature Digital Library, indexing over 200,000 computer science articlesReVision: Reviewing Vision in the Web!RIKEN: The Institute of Physical and Chemical Research, JapanSalford SystemsSANS Studies of Artificial Neural Systems, at the Royal Institute of Technology, Sweden Santa-Fe InstituteScirus: a search engine locating scientific information on the InternetSecond Moment: The News and Business Resource for Applied AnalyticsSEL-HPC Article Archive has sections for neural networks, distributed AI, theorem proving, and a variety of other computer science topicsSOAR Project at University of Southern CaliforniaSociety for AI and StatisticsSVM of ANU CanberraSVM of Bell LabsSVM of GMD-First BerlinSVM of MITSVM of Royal Holloway CollegeSVM of University of SouthamptonSVM-workshop at NIPS97TechOnLine: TechOnLine University offers free online courses and lecturesUCI Machine Learning GroupUMASS Distributed Artificial Intelligence LaboratoryUTCS Neural Networks Research Group of Artificial Intelligence Lab, Computer Science Department, University of Texas at AustinVivisimo Document Clustering: a powerful search engine which returns clustered results Worcester Polytechnic Institute Artificial Intelligence Research Group (AIRG)Xerion neural network simulator developed and used by the connectionist group at the University of TorontoYale's CTAN Advanced Technology Center for Theoretical and Applied Neuroscience ZooLand: Artificial Life Resource。