http://www.bibsonomy.org/burst/concept/tag/TutorialBibSonomy BuRST Feed for /concept/tag/Tutorial2009-01-06T14:59:00+01:00http://www.bibsonomy.org/bibtex/279e227be1a6a8629cff901ca4f330ba0/chriskoernerchriskoerner2008-11-16T18:53:17+01:00tutorial SVM <span style="color:#555555;">Christopher J. C. <a href="http://www.bibsonomy.org/author/Burges">Burges</a> </span><em>Data Mining and Knowledge Discovery</em>(<em>1998</em>)Sun Nov 16 18:53:17 CET 2008Data Mining and Knowledge Discovery121--167A Tutorial on Support Vector Machines for Pattern Recognition21998tutorial SVM http://www.bibsonomy.org/bibtex/279e227be1a6a8629cff901ca4f330ba0/hkortehkorte2008-11-05T15:04:57+01:00tutorial svm <span style="color:#555555;">Christopher J. C. <a href="http://www.bibsonomy.org/author/Burges">Burges</a> </span><em>Data Mining and Knowledge Discovery</em>(<em>1998</em>)Wed Nov 05 15:04:57 CET 2008Data Mining and Knowledge Discovery121--167A Tutorial on Support Vector Machines for Pattern Recognition21998tutorial svm Graph kernels and Gaussian processes for relational reinforcement learning - CiteSeerXhttp://www.bibsonomy.org/bibtex/25595506decc2c340ea7a98e598fe22b9/mkroellmkroell2008-09-16T16:23:32+02:00graph tutorial ReinforcementLearning kernel GaussianProcesses <span style="color:#555555;">Thomas <a href="http://www.bibsonomy.org/author/Gartner">Gartner</a> and Kurt <a href="http://www.bibsonomy.org/author/Driessens">Driessens</a> and Jan <a href="http://www.bibsonomy.org/author/Ramon">Ramon</a> </span><em>Machine Learning, </em><em>page146--163. </em><em>Springer, </em>(<em>2003</em>)Tue Sep 16 16:23:32 CEST 2008Machine Learning146--163Graph kernels and Gaussian processes for relational reinforcement learning2003graph tutorial ReinforcementLearning kernel GaussianProcesses ROC Graphs: Notes and Practical Considerations for Researchershttp://www.bibsonomy.org/bibtex/2c580a50d58db5cd78d7dc5ab3cbd2a29/hothohotho2008-08-15T14:50:10+02:00roc auc tutorial evaluation <span style="color:#555555;">T. <a href="http://www.bibsonomy.org/author/Fawcett">Fawcett</a> </span><em>HP Laboratories, </em>(<em>2004</em>)Fri Aug 15 14:50:10 CEST 2008Tech Report HPL-2003-4ROC Graphs: Notes and Practical Considerations for Researchers2004roc auc tutorial evaluation An Introduction to the Conjugate Gradient Method Without the Agonizing Painhttp://www.bibsonomy.org/bibtex/2c61c3025579ab2efb05a1c76a187289a/rwozrwoz2008-08-02T18:44:17+02:00da tutorial shewchuk numerical toread conjugate introduction optimisation gradient <span style="color:#555555;">Jonathan R <a href="http://www.bibsonomy.org/author/Shewchuk">Shewchuk</a> </span><em>Pittsburgh, PA, USA, </em>(<em>1994</em>)Sat Aug 02 18:44:17 CEST 2008Pittsburgh, PA, USAAn Introduction to the Conjugate Gradient Method Without the Agonizing Pain1994da tutorial shewchuk numerical toread conjugate introduction optimisation gradient The Conjugate Gradient Method is the most prominent iterative method for solving sparse systems of linear equations. Unfortunately, many textbook treatments of the topic are written so that even their own authors would be mystified, if they bothered to read their own writing. For this reason, an understanding of the method has been reserved for the elite brilliant few who have painstakingly decoded the mumblings of their forebears. Nevertheless, the Conjugate Gradient Method is a composite of simple, elegant ideas that almost anyone can understand. Of course, a reader as intelligent as yourself will learn them almost effortlessly. The idea of quadratic forms is introduced and used to derive the methods of Steepest Descent, Conjugate Directions, and Conjugate Gradients. Eigenvectors are explained and used to examine the convergence of the Jacobi Method, Steepest Descent, and Conjugate Gradients. Other topics include preconditioning and the nonlinear Conjugate Gradient Method. I have taken pains to make this article easy to read. Sixty-two illustrations are provided. Dense prose is avoided. Concepts are explained in several different ways. Most equations are coupled with an intuitive interpretation.http://www.bibsonomy.org/bibtex/263e329efec488121475e309f232d2dc5/grongron2008-07-24T21:03:18+02:00Tutorial CaseStudy <span style="color:#555555;">Steve <a href="http://www.bibsonomy.org/author/Easterbrook">Easterbrook</a> and Jorge <a href="http://www.bibsonomy.org/author/Aranda">Aranda</a> </span><em>Tutorial at the 28th International Conference on Software Engineering, </em><em>May2006. </em>Thu Jul 24 21:03:18 CEST 2008Tutorial at the 28th International Conference on Software EngineeringMayCase Studies for Software Engineers2006Tutorial CaseStudy The purpose of this full-day tutorial is to demonstrate the correct use and interpretation of case studies in software engineering. It will help software engineers identify and avoid common mistakes by giving them a solid grounding in the fundamentals of case studies as a research method. Using an equal blend of lecture and discussion, it aims to provide software engineers with a foundation for conducting, reviewing, and reading case studies. For researchers, this tutorial will provide a starting point for learning how to conduct case studies. They will be able to find, assess, and apply appropriate resources at their home institution. For reviewers, the tutorial will provide guidance on how to judge the quality and validity of reported case studies. They will be able to use the criteria presented in this tutorial to assess whether research papers based on case studies are suitable for publication, allowing them to raise the quality of publications and give appropriate feedback to authors. For practitioners, the tutorial will provide a better awareness of how to interpret the claims made by researchers about new software engineering methods and tools. Practitioners will also gain deeper insights into the roles they can play in designing and conducting case studies in collaborative research projects. As well, they will read case studies more effectively and be better able to identify results suitable for use in their workplace.http://www.bibsonomy.org/bibtex/29e59bfc95e50ce0719e21d475734a368/pprettpprett2008-06-17T16:01:02+02:00tutorial pca, <span style="color:#555555;">Jonathon <a href="http://www.bibsonomy.org/author/Shlens">Shlens</a> </span><em>http://www.snl.salk.edu/~shlens/pub/notes/pca.pdf, </em><em>December2005. </em>Tue Jun 17 16:01:02 CEST 2008http://www.snl.salk.edu/~shlens/pub/notes/pca.pdfDecemberA Tutorial on Principal Component Analysis2005tutorial pca, http://www.bibsonomy.org/bibtex/2ad2a33b52e690eaf15da04fff7f12755/pprettpprett2008-06-17T16:01:02+02:00tutorial pattern, recognition, svm, <span style="color:#555555;">Christopher J. C. <a href="http://www.bibsonomy.org/author/Burges">Burges</a> </span><em>Data Mining and Knowledge Discovery</em><em>2(2):121--167</em>(<em>1998</em>)Tue Jun 17 16:01:02 CEST 2008Data Mining and Knowledge Discovery2121--167A Tutorial on Support Vector Machines for Pattern Recognition21998tutorial pattern, recognition, svm, The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct...http://www.bibsonomy.org/bibtex/200620017c8fe5cfa2994c843192faf51/cschenkcschenk2008-06-06T15:17:59+02:00bibtex tutorial latex howto <span style="color:#555555;">Nicolas <a href="http://www.bibsonomy.org/author/Markey">Markey</a> </span><em>October2005. </em>Fri Jun 06 15:17:59 CEST 2008OctoberTame the BeaST2005bibtex tutorial latex howto This 45-page tutorial presents and explains, as clearly and exhaustively as possible, what BibTEX can do. Indeed, BibTEX manuals, essentially two documents by its author [Pat88a, Pat88b] and chapters in some LATEX books [Lam97, GMS93, MGB+04, ...], are often short and incomplete. The capital letters “BST” in the title represent the standard extension of BibTEX style files. “B to X” means that I tried to be as complete as possible. Don’t hesitate to e-mail me you TEXnical as well as (mis)spelling remarks.ROC Graphs: Notes and Practical Considerations for Researchershttp://www.bibsonomy.org/bibtex/2819643be78dd30c066ebf6a97c5480df/beatebeate2008-06-04T13:47:18+02:00roc data-mining tutorial evaluation <span style="color:#555555;">T. <a href="http://www.bibsonomy.org/author/Fawcett">Fawcett</a> </span>(<em>2004</em>)Wed Jun 04 13:47:18 CEST 2008ROC Graphs: Notes and Practical Considerations for Researchers2004roc data-mining tutorial evaluation http://www.bibsonomy.org/bibtex/2c04ef97dc3c3de168e684c3e4abe061b/jiljil2008-05-28T19:14:22+02:00guide tutorial svm libsvm <span style="color:#555555;">Chih-Wei <a href="http://www.bibsonomy.org/author/Hsu">Hsu</a> and Chih-Chung <a href="http://www.bibsonomy.org/author/Chang">Chang</a> and Chih-Jen <a href="http://www.bibsonomy.org/author/Lin">Lin</a> </span><em>Department of Computer Science, National Taiwan University, </em>(<em>2003</em>)Wed May 28 19:14:22 CEST 2008A Practical Guide to Support Vector Classification2003guide tutorial svm libsvm Support vector machine (SVM) is a popular technique for classification. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we propose a simple procedure, which usually gives reasonable results.http://www.bibsonomy.org/bibtex/22979c91ccbf9d406db77d7e701ea6f94/tmalsburgtmalsburg2008-05-26T21:21:38+02:00patternrecognition dimensionalityreduction machinelearning unsupervised tutorial sfa emdeddings <span style="color:#555555;">Pietro <a href="http://www.bibsonomy.org/author/Berkes">Berkes</a> </span>(<em>2005</em>)Mon May 26 21:21:38 CEST 2008Cognitive Sciences EPrint Archive{Pattern Recognition with Slow Feature Analysis}2005patternrecognition dimensionalityreduction machinelearning unsupervised tutorial sfa emdeddings Slow feature analysis (SFA) is a new unsupervised algorithm to learn nonlinear functions that ex tract slowly varying signals out of the input data. In this paper we describe its application to pattern recognition. In this context in order to be slowly varying the functions learned by SFA need to respond similarly to the patterns belonging to the same class. We prove that, given input patterns belonging to C non-overlapping classes and a large enough function space, the optimal solution consists of C − 1 output signals that are constant for each individual class. As a consequence, their output provides a feature space suitable to perform classification with simple methods, such as Gaussian classifiers. We then show as an example the application of SFA to the MNIST handwritten digits database. The performance of SFA is comparable to that of other established algorithms. Finally, we suggest some possible extensions to the proposed method. Our approach is in particular attractive because for a given input signal and a fixed function space it has no parameters, it is easy to implement and apply, and it has low memory requirements and high speed during recognition. SFA finds the global solution (within the considered function space) in a single iteration without convergence issues. Moreover, the proposed method is completely problem-independent.http://www.bibsonomy.org/bibtex/2690d9e63f68ff0a0f9ad2a964afcbd36/jgomezdansjgomezdans2008-05-15T11:40:15+02:00assimilation model tutorial bayes uncertainty statistics <span style="color:#555555;">Christopher K. <a href="http://www.bibsonomy.org/author/Wikle">Wikle</a> and Mark L. <a href="http://www.bibsonomy.org/author/Berliner">Berliner</a> </span><em>Physica D: Nonlinear Phenomena</em><em>230(1-2):1--16</em><em>June2007. </em>Thu May 15 11:40:15 CEST 2008Data AssimilationPhysica D: Nonlinear PhenomenaJune1-21--16A Bayesian tutorial for data assimilation2302007assimilation model tutorial bayes uncertainty statistics Data assimilation is the process by which observational data are fused with scientific information. The Bayesian paradigm provides a coherent probabilistic approach for combining information, and thus is an appropriate framework for data assimilation. Viewing data assimilation as a problem in Bayesian statistics is not new. However, the field of Bayesian statistics is rapidly evolving and new approaches for model construction and sampling have been utilized recently in a wide variety of disciplines to combine information. This article includes a brief introduction to Bayesian methods. Paying particular attention to data assimilation, we review linkages to optimal interpolation, kriging, Kalman filtering, smoothing, and variational analysis. Discussion is provided concerning Monte Carlo methods for implementing Bayesian analysis, including importance sampling, particle filtering, ensemble Kalman filtering, and Markov chain Monte Carlo sampling. Finally, hierarchical Bayesian modeling is reviewed. We indicate how this approach can be used to incorporate significant physically based prior information into statistical models, thereby accounting for uncertainty. The approach is illustrated in a simplified advection-diffusion model.http://www.bibsonomy.org/bibtex/2b7cf853e8635bd2887e8dea3d9e10ccb/jiljil2008-05-04T17:00:45+02:00mathematik math lagrange kkt background tutorial svm mathe <span style="color:#555555;">J.P. <a href="http://www.bibsonomy.org/author/Lewis">Lewis</a> </span><em>CGIT Lab / IMSC, </em>(<em>2004</em>)Sun May 04 17:00:45 CEST 2008A Short SVM (Support Vector Machine) Tutorial2004mathematik math lagrange kkt background tutorial svm mathe http://www.bibsonomy.org/bibtex/2ad2a33b52e690eaf15da04fff7f12755/jiljil2008-05-04T16:52:21+02:00lagrange herleitung burges kkt tutorial deduction svm <span style="color:#555555;">Christopher J. C. <a href="http://www.bibsonomy.org/author/Burges">Burges</a> </span><em>Data Mining and Knowledge Discovery</em><em>2(2):121-167</em>(<em>1998</em>)Sun May 04 16:52:21 CEST 2008Data Mining and Knowledge Discovery2121-167A Tutorial on Support Vector Machines for Pattern Recognition21998lagrange herleitung burges kkt tutorial deduction svm A tutorial on ν-support vector machineshttp://www.bibsonomy.org/bibtex/24849dc190c907bcb507aece582e76353/jiljil2008-05-02T00:49:56+02:00tutorial svm trick kernel <span style="color:#555555;">Pai-Hsuen <a href="http://www.bibsonomy.org/author/Chen">Chen</a> and Chih-Jen <a href="http://www.bibsonomy.org/author/Lin">Lin</a> and Bernhard <a href="http://www.bibsonomy.org/author/Sch\&#034;{o}lkopf">Sch&#246;lkopf</a> </span><em>Appl. Stoch. Model. Bus. Ind.</em><em>21(2):111--136</em>(<em>2005</em>)Fri May 02 00:49:56 CEST 2008Chichester, UK, UKAppl. Stoch. Model. Bus. Ind.2111--136A tutorial on \ν-support vector machines: Research Articles212005tutorial svm trick kernel We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called ν-SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.Parts of the present article are based on [1].http://www.bibsonomy.org/bibtex/24b05c8ddd8bafaa889c36aa9eb2929f9/tmalsburgtmalsburg2008-04-27T12:41:57+02:00tutorial dynamicfieldtheory robotics motorcontrol <span style="color:#555555;">W. <a href="http://www.bibsonomy.org/author/Erlhagen">Erlhagen</a> and E. <a href="http://www.bibsonomy.org/author/Bicho">Bicho</a> </span><em>Journal of Neural Engineering</em>(<em>2006</em>)Sun Apr 27 12:41:57 CEST 2008Journal of Neural Engineering36-54{The dynamic neural field approach to cognitive robotics}32006tutorial dynamicfieldtheory robotics motorcontrol Abstract This tutorial presents an architecture for autonomous robots to generate behavior in joint action tasks. To efficiently interact with another agent in solving a mutual task, a robot should be endowed with cognitive skills such as memory, decision making, action understanding and prediction. The proposed architecture is strongly inspired by our current understanding of the processing principles and the neuronal circuitry underlying these functionalities in the primate brain. As a mathematical framework, we use a coupled system of dynamic neural fields, each representing the basic functionality of neuronal populations in different brain areas. It implements goal-directed behavior in joint action as a continuous process that builds on the interpretation of observed movements in terms of the partner’s action goal. We validate the architecture in two experimental paradigms: (1) a joint search task; (2) a reproduction of an observed or inferred end state of a grasping–placing sequence. We also review some of the mathematical results about dynamic neural fields that are important for the implementation work. dblphttp://www.bibsonomy.org/bibtex/2268d4f4528334efaa69591ecb6dad20f/mmcglohommcgloho2008-04-07T20:11:09+02:00tutorial <span style="color:#555555;">Christos <a href="http://www.bibsonomy.org/author/Faloutsos">Faloutsos</a> and Tamara G. <a href="http://www.bibsonomy.org/author/Kolda">Kolda</a> and Jimeng <a href="http://www.bibsonomy.org/author/Sun">Sun</a> </span><em>SIGMOD Conference, </em><em>page1174. </em><em>ACM, </em>(<em>2007</em>)Mon Apr 07 20:11:09 CEST 2008SIGMOD Conferenceconf/sigmod/20071174Mining large graphs and streams using matrix and tensor tools.2007tutorial Whenever the eqnarray environment appears in a question or an example of a problem on comp.text.tex or the TEXhax mailing list there is a large chance that someone will tell the poster not to use eqnarray. This article will provide some examples of why many of us consider eqnarray to be harmful and why it should not be used.http://www.bibsonomy.org/bibtex/2d703be10bc7aebb83cfda2d0edfe47d8/cschenkcschenk2008-04-02T17:49:09+02:00journal eqnarray harmful paper practex amsmath tutorial latex equations toread texhax <span style="color:#555555;">Lars <a href="http://www.bibsonomy.org/author/Madsen">Madsen</a> </span><em>PracTeX</em>(<em>2006</em>)Wed Apr 02 17:49:09 CEST 2008PracTeX4Avoid eqnarray!2006journal eqnarray harmful paper practex amsmath tutorial latex equations toread texhax Angeregt durch eine Diskussion in der deutschsprachigen TEX-Newsgroup über das wiederholte Auftauchen von veralteten und „schlechten“ Paketen und Befehlen, habe ich mich entschlossen, diese kleine Übersicht zu schreiben. Ich versuche in diesem Artikel die gängigsten Fehler zu zeigen und Alternativen anzubieten.http://www.bibsonomy.org/bibtex/29cec133a6791f0e5af303036cf5eca06/cschenkcschenk2008-04-02T17:45:25+02:00register sünde lang:de tutorial latex toread tipps <span style="color:#555555;">Mark <a href="http://www.bibsonomy.org/author/Trettin">Trettin</a> </span><em>Dezember2004. </em><em>Version 1.8 vom 19. Dezember 2004 . </em>Wed Apr 02 17:45:25 CEST 2008DezemberVersion 1.8 vom 19. Dezember 2004Das LATEX 2ε - Sündenregister2004register sünde lang:de tutorial latex toread tipps