http://www.bibsonomy.org/burst/tag/mlBibSonomy BuRST Feed for /tag/ml2008-07-21T00:44:04+02:00http://www.bibsonomy.org/bibtex/2e53237069d3e653063d791afb870e010/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00ML <span style="color:#555555;">Ming <a href="http://www.bibsonomy.org/author/Li">Li</a> </span><em>NeuroCOLT technical report series, </em><em>NC-TR-95-052. </em><em>Royal Holloway and Bedford New College, University of London, </em><em>Surrey, UK, </em><em>September1995. </em>Thu Jun 19 17:35:00 CEST 2008Surrey, UKSeptemberNC-TR-95-052Computational Machine Learning in Theory and PraxisNeuroCOLT technical report series1995ML In the last few decades a computational approach to machine learning has emerged based on paradigms from recursion theory and the theory of computation. Such ideas include learning in the limit, learning by enumeration, and probably approximately correct (pac) learning. These models usually are not suitable in practical situations. In contrast, statistics based inference methods have enjoyed a long and distinguished career. Currently, Bayesian reasoning in various forms, minimum message length (MML) and minimum description length (MDL), are widely applied approaches. They are the tools to use with particular machine learning praxis such as simulated annealing, genetic algorithms, genetic programming, artificial neural networks, and the like. These statistical inference methods select the hypothesis which minimizes the sum of the length of the description of the hypothesis (also called `model') and the length of the description of the data relative to the hypothesis. It app...http://www.bibsonomy.org/bibtex/257ade2d873735d4c54d44365dafa7605/michimichi2008-06-18T14:57:01+02:00weka ml datamining clustering <span style="color:#555555;">Ian H. <a href="http://www.bibsonomy.org/author/Witten">Witten</a> and Eibe <a href="http://www.bibsonomy.org/author/Frank">Frank</a> </span><em>Morgan Kaufmann Series in Data Management Sys </em><em>Morgan Kaufmann, </em><em>Second edition, </em><em>June2005. </em>Wed Jun 18 14:57:01 CEST 2008SecondPaperbackJuneMorgan Kaufmann Series in Data Management SysData Mining: Practical Machine Learning Tools and Techniques2005weka ml datamining clustering Institut AIFB - Publikation: AEON - An Approach to the Automatic Evaluation of Ontologieshttp://www.bibsonomy.org/bibtex/2ea55fe7088ef25cdf060d30d94a09e26/hothohotho2008-06-17T22:30:50+02:00automatic ml ontology evaluation sw 2008 myown <span style="color:#555555;">Johanna <a href="http://www.bibsonomy.org/author/Völker">V&#246;lker</a> and Denny <a href="http://www.bibsonomy.org/author/Vrandecic">Vrandecic</a> and York <a href="http://www.bibsonomy.org/author/Sure">Sure</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Journal of Applied Ontology</em>(<em>2008</em>) <em>to appear . </em>Tue Jun 17 22:30:50 CEST 2008Journal of Applied Ontologyto appearAEON - An Approach to the Automatic Evaluation of Ontologies2008automatic ml ontology evaluation sw 2008 myown http://www.bibsonomy.org/bibtex/2bd69b7eb9e339365f0f0d5338c6e17c4/pprettpprett2008-06-17T16:01:02+02:00learning2rank, ml <span style="color:#555555;">William W. <a href="http://www.bibsonomy.org/author/Cohen">Cohen</a> and Robert E. <a href="http://www.bibsonomy.org/author/Schapire">Schapire</a> and Yoram <a href="http://www.bibsonomy.org/author/Singer">Singer</a> </span><em>Advances in Neural Information Processing Systems, </em><em>10, </em><em>The MIT Press, </em>(<em>1998</em>)Tue Jun 17 16:01:02 CEST 2008Advances in Neural Information Processing SystemsLearning to Order Things101998learning2rank, ml There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a preference function, of the form PREF(u; v), which indicates whether it is advisable to rank u before v. New instances are...http://www.bibsonomy.org/bibtex/27dc655b3527c9d0dd63651727e844ca9/draganigajicdraganigajic2008-06-13T00:37:05+02:00ml intro <span style="color:#555555;">Matthias <a href="http://www.bibsonomy.org/author/Felleisen">Felleisen</a> and Daniel P. <a href="http://www.bibsonomy.org/author/Friedman">Friedman</a> </span><em>The MIT Press, </em><em>December1997. </em>Fri Jun 13 00:37:05 CEST 2008December200The Little MLer1997ml intro http://www.bibsonomy.org/bibtex/22c3b24830a406cb223ebd00d38b3bbec/draganigajicdraganigajic2008-06-13T00:37:05+02:00ml fp <span style="color:#555555;">Lawrence C. <a href="http://www.bibsonomy.org/author/Paulson">Paulson</a> </span><em>Cambridge University Press, </em><em>2 edition, </em><em>June1996. </em>Fri Jun 13 00:37:05 CEST 20082June496ML for the Working Programmer1996ml fp http://www.bibsonomy.org/bibtex/2d3156e8296bdb0355a3212d9242268a0/draganigajicdraganigajic2008-06-13T00:37:05+02:00ml compiling <span style="color:#555555;">Andrew W. <a href="http://www.bibsonomy.org/author/Appel">Appel</a> </span><em>Cambridge University Press, </em><em>New Ed edition, </em><em>July2004. </em>Fri Jun 13 00:37:05 CEST 2008New EdJuly548Modern Compiler Implementation in ML2004ml compiling http://www.bibsonomy.org/bibtex/2ed3c317108b3f7ef8831c249d18e0c34/draganigajicdraganigajic2008-06-13T00:37:05+02:00intro textbook ml <span style="color:#555555;">Jeffrey D. <a href="http://www.bibsonomy.org/author/Ullman">Ullman</a> </span><em>Prentice Hall, </em><em>2 edition, </em>(<em>1998</em>)Fri Jun 13 00:37:05 CEST 20082383Elements of ML Programming, ML97 Edition1998intro textbook ml http://www.bibsonomy.org/bibtex/281c18c6d7708728770bdf53b3cb375e5/draganigajicdraganigajic2008-06-13T00:37:05+02:00fp ml <span style="color:#555555;">Guy <a href="http://www.bibsonomy.org/author/Cousineau">Cousineau</a> and Michel <a href="http://www.bibsonomy.org/author/Mauny">Mauny</a> </span><em>Cambridge University Press, </em><em>English Ed edition, </em><em>October1998. </em>Fri Jun 13 00:37:05 CEST 2008English EdOctober460The Functional Approach to Programming1998fp ml http://www.bibsonomy.org/bibtex/2e053389338dafde2946ec585bc35a48e/stummestumme2008-06-09T12:37:50+02:00myown 2007 ir mining ki introduction ml web <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> (eds.). </span><em>3, </em>(<em>2007</em>)Mon Jun 09 12:37:50 CEST 2008Künstliche Intelligenz35-8Themenheft Web Mining2007myown 2007 ir mining ki introduction ml web http://www.bibsonomy.org/bibtex/2e9535ec82afa53f44a1b37704aa9a71f/stummestumme2008-06-09T12:36:33+02:00mining ir web ml ki myown introduction 2007 <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>K&#252;nstliche Intelligenz</em>(<em>2007</em>)Mon Jun 09 12:36:33 CEST 2008Künstliche Intelligenz35-8Mining the World Wide Web -- Methods, Ap- plications, and Perspectives2007mining ir web ml ki myown introduction 2007 http://www.bibsonomy.org/bibtex/24d04bfd2f6789c757bfb2aa8191a6edb/sb3000sb30002008-05-16T16:51:20+02:00structured-data propositionalization ml <span style="color:#555555;">Chad M. <a href="http://www.bibsonomy.org/author/Cumby">Cumby</a> and Dan <a href="http://www.bibsonomy.org/author/Roth">Roth</a> </span><em>Proceedings of the 12th International Conference on Inductive Logic Programming (ILP 2002), July 9--11, 2002, Sydney, Australia --- Revised Papers, </em><em>2583, </em><em>page32--47. </em><em>Springer, Berlin--Heidelberg, Germany, </em>(<em>2003</em>)Fri May 16 16:51:20 CEST 2008Proceedings of the 12th International Conference on Inductive Logic Programming (ILP 2002), July 9--11, 2002, Sydney, Australia --- Revised Papers258332--47Lecture Notes in Computer ScienceLearning with Feature Description Logics2003structured-data propositionalization ml http://www.bibsonomy.org/bibtex/2f58afc5c9793fcc8ad8389824e57984c/sb3000sb30002008-05-16T16:17:42+02:00ml statistics <span style="color:#555555;">Trevor <a href="http://www.bibsonomy.org/author/Hastie">Hastie</a> and Robert <a href="http://www.bibsonomy.org/author/Tibshirani">Tibshirani</a> and Jerome <a href="http://www.bibsonomy.org/author/Friedman">Friedman</a> </span><em>Springer Series in Statistics </em><em>Springer New York Inc., </em><em>New York, NY, USA, </em>(<em>2001</em>)Fri May 16 16:17:42 CEST 2008New York, NY, USASpringer Series in StatisticsThe Elements of Statistical Learning2001ml statistics http://www.bibsonomy.org/bibtex/252d71136494540d7f5712edc0dc5b3dc/sb3000sb30002008-05-16T13:56:34+02:00ml <span style="color:#555555;">Thomas <a href="http://www.bibsonomy.org/author/Mitchell">Mitchell</a> </span><em>McGraw-Hill, </em><em>New York, NY, USA, </em><em>October1997. </em>Fri May 16 13:56:34 CEST 2008New York, NY, USAPaperbackOctoberMachine Learning1997ml http://www.bibsonomy.org/bibtex/28f5f1af12b759183d0316581dca8f9e7/marcoalvarezmarcoalvarez2008-05-14T11:33:59+02:00ML <span style="color:#555555;">Tom M. <a href="http://www.bibsonomy.org/author/Mitchell">Mitchell</a> </span><em>CMU-ML-06-108. </em><em>Carnegie Mellon University - ML Department, </em><em>July2006. </em>Wed May 14 11:33:59 CEST 2008JulyCMU-ML-06-108The discipline of machine learning2006ML Over the past 50 years the study of Machine Learning has grown from the efforts of a handful of computer engineers exploring whether computers could learn to play games, and a field of Statistics that largely ignored computational considerations, to a broad discipline that has produced fundamental statistical-computational theories of learning processes, has designed learning algorithms that are routinely used in commercial systems for speech recognition, computer vision, and a variety of other tasks, and has spun off an industry in data mining to discover hidden regularities in the growing volumes of online data. This document provides a brief and personal view of the discipline that has emerged as Machine Learning, the fundamental questions it addresses, its relationship to other sciences and society, and where it might be headed.http://www.bibsonomy.org/bibtex/2c4c598c0587dfb2b57f1ed9f0082484d/marcoalvarezmarcoalvarez2008-05-14T11:33:59+02:00ML Classification <span style="color:#555555;">Rich <a href="http://www.bibsonomy.org/author/Caruana">Caruana</a> and Alexandru <a href="http://www.bibsonomy.org/author/Niculescu-Mizil">Niculescu-Mizil</a> </span><em>International Conference on Machine Learning, </em><em>page161--168. </em>(<em>2006</em>)Wed May 14 11:33:59 CEST 2008International Conference on Machine Learning161--168An empirical comparison of supervised learning algorithms2006ML Classification A number of supervised learning methods have been introduced in the last decade. Unfortunately, the last comprehensive empirical evaluation of supervised learning was the Statlog Project in the early 90's. We present a large-scale empirical comparison between ten supervised learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps. We also examine the effect that calibrating the models via Platt Scaling and Isotonic Regression has on their performance. An important aspect of our study is the use of a variety of performance criteria to evaluate the learning methods.http://www.bibsonomy.org/bibtex/22de98c2b635f36c137e25256e8c235e0/sb3000sb30002008-04-25T14:59:33+02:00sb ir sb-editor ml <span style="color:#555555;">Stephan <a href="http://www.bibsonomy.org/author/Bloehdorn">Bloehdorn</a> and Wray <a href="http://www.bibsonomy.org/author/Buntine">Buntine</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> (eds.). </span>(<em>2005</em>) <em>Workshop at the 22nd International Conference on Machine Learning (ICML 2005) . </em>Fri Apr 25 14:59:33 CEST 2008Workshop at the 22nd International Conference on Machine Learning (ICML 2005) Proceedings of the Workshop on Learning in Web Search (LWS 2005) 2005sb ir sb-editor ml http://www.bibsonomy.org/bibtex/2e7cac98b75f3399597b8bb9fac527341/smichasmicha2008-04-23T22:05:04+02:00ML estimation <span style="color:#555555;">Byung-Gook <a href="http://www.bibsonomy.org/author/Lee">Lee</a> and Ki Yong <a href="http://www.bibsonomy.org/author/Lee">Lee</a> and Souguil <a href="http://www.bibsonomy.org/author/Ann">Ann</a> </span><em>Signal Processing</em><em>46(1):1--14</em><em>Sep1995. </em>Wed Apr 23 22:05:04 CEST 2008Signal ProcessingSep11--14An EM-based approach for parameter enhancement with an application to speech signals461995ML estimation http://www.bibsonomy.org/bibtex/203d349d70b578ca9ac3155f661151868/hothohotho2008-04-23T13:56:47+02:00ml dm 2008 spam mining classification folksonomy social bookmarking myown <span style="color:#555555;">Beate <a href="http://www.bibsonomy.org/author/Krause">Krause</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web, </em>(<em>2008</em>)Wed Apr 23 13:56:47 CEST 2008Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the WebThe Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems2008ml dm 2008 spam mining classification folksonomy social bookmarking myown http://www.bibsonomy.org/bibtex/2502a6bf8f280f1c850a46cece43cc7a7/sb3000sb30002008-03-28T12:37:19+01:00ml logic <span style="color:#555555;">J.W. <a href="http://www.bibsonomy.org/author/Lloyd">Lloyd</a> </span><em>Springer, </em><em>Berlin--Heidelberg, Germany, </em>(<em>2003</em>)Fri Mar 28 12:37:19 CET 2008Berlin--Heidelberg, GermanyLogic for Learning: Learning Comprehensible Theories from Structured Data2003ml logic