@techreport{oai:CiteSeerPSU:387590, title = {Computational Machine Learning in Theory and Praxis}, address = {Surrey, UK}, annote = {The Pennsylvania State University CiteSeer Archives}, author = {Ming Li}, institution = {Royal Holloway and Bedford New College, University of London}, month = {September}, number = {NC-TR-95-052}, type = {NeuroCOLT technical report series}, year = 1995, url = {http://www.neurocolt.com/abs/1995/../../tech_reps/1995/nc-tr-95-052.ps.gz}, oai = {oai:CiteSeerPSU:387590}, rights = {unrestricted}, language = {en}, notes = {not a CP paper}, size = {20 pages}, abstract = {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...}, biburl = {http://www.bibsonomy.org/bibtex/2e53237069d3e653063d791afb870e010/brazovayeye}, keywords = {ML} } @book{DataMiningWeka, title = {Data Mining: Practical Machine Learning Tools and Techniques}, author = {Ian H. Witten and Eibe Frank}, edition = {Second}, howpublished = {Paperback}, month = {June}, publisher = {Morgan Kaufmann}, series = {Morgan Kaufmann Series in Data Management Sys}, year = 2005, id = {340715}, priority = {0}, isbn = {0120884070}, biburl = {http://www.bibsonomy.org/bibtex/257ade2d873735d4c54d44365dafa7605/michi}, keywords = {datamining clustering ml weka} } @article{voelker2008aeon, title = {AEON - An Approach to the Automatic Evaluation of Ontologies}, author = {Johanna Völker and Denny Vrandecic and York Sure and Andreas Hotho}, journal = {Journal of Applied Ontology}, note = {to appear}, year = 2008, url = {http://ontoware.org/projects/aeon/}, description = {Institut AIFB - Publikation: AEON - An Approach to the Automatic Evaluation of Ontologies}, biburl = {http://www.bibsonomy.org/bibtex/2ea55fe7088ef25cdf060d30d94a09e26/hotho}, keywords = {evaluation automatic ontology ml myown 2008 sw} } @inproceedings{citeulike:415391, title = {Learning to Order Things}, author = {William W. Cohen and Robert E. Schapire and Yoram Singer}, booktitle = {Advances in Neural Information Processing Systems}, editor = {Michael I. Jordan and Michael J. Kearns and Sara A. Solla}, publisher = {The MIT Press}, volume = 10, year = 1998, url = {http://citeseer.ist.psu.edu/cohen98learning.html}, id = {415391}, priority = {3}, at = {2008-06-16 15:08:00}, abstract = {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...}, biburl = {http://www.bibsonomy.org/bibtex/2bd69b7eb9e339365f0f0d5338c6e17c4/pprett}, keywords = {learning2rank, ml} } @book{felleisen_little_1997, title = {The Little MLer}, author = {Matthias Felleisen and Daniel P. Friedman}, month = {December}, pages = 200, publisher = {The MIT Press}, year = 1997, isbn = {026256114X}, biburl = {http://www.bibsonomy.org/bibtex/27dc655b3527c9d0dd63651727e844ca9/draganigajic}, keywords = {intro ml} } @book{paulson_ml_1996, title = {ML for the Working Programmer}, author = {Lawrence C. Paulson}, edition = 2, month = {June}, pages = 496, publisher = {Cambridge University Press}, year = 1996, isbn = {052156543X}, biburl = {http://www.bibsonomy.org/bibtex/22c3b24830a406cb223ebd00d38b3bbec/draganigajic}, keywords = {fp ml} } @book{appel_modern_2004, title = {Modern Compiler Implementation in ML}, author = {Andrew W. Appel}, edition = {New Ed}, month = {July}, pages = 548, publisher = {Cambridge University Press}, year = 2004, isbn = {0521607647}, biburl = {http://www.bibsonomy.org/bibtex/2d3156e8296bdb0355a3212d9242268a0/draganigajic}, keywords = {compiling ml} } @book{ullman_elements_1998, title = {Elements of ML Programming, ML97 Edition}, author = {Jeffrey D. Ullman}, edition = 2, pages = 383, publisher = {Prentice Hall}, year = 1998, isbn = {0137903871}, biburl = {http://www.bibsonomy.org/bibtex/2ed3c317108b3f7ef8831c249d18e0c34/draganigajic}, keywords = {ml intro textbook} } @book{cousineau_functional_1998, title = {The Functional Approach to Programming}, author = {Guy Cousineau and Michel Mauny}, edition = {English Ed}, month = {October}, pages = 460, publisher = {Cambridge University Press}, year = 1998, isbn = {0521576814}, biburl = {http://www.bibsonomy.org/bibtex/281c18c6d7708728770bdf53b3cb375e5/draganigajic}, keywords = {ml fp} } @proceedings{themenheft2007webmining, title = {Themenheft Web Mining}, editor = {Andreas Hotho and Gerd Stumme}, journal = {Künstliche Intelligenz}, number = 3, pages = {5-8}, year = 2007, url = {http://www.kuenstliche-intelligenz.de/index.php?id=7758}, biburl = {http://www.bibsonomy.org/bibtex/2e053389338dafde2946ec585bc35a48e/stumme}, keywords = {2007 myown ki ml web mining ir introduction} } @article{themenheft2007webmining, title = {Mining the World Wide Web -- Methods, Ap- plications, and Perspectives}, author = {Andreas Hotho and Gerd Stumme}, journal = {Künstliche Intelligenz}, number = 3, pages = {5-8}, year = 2007, url = {http://www.kuenstliche-intelligenz.de/index.php?id=7758}, biburl = {http://www.bibsonomy.org/bibtex/2e9535ec82afa53f44a1b37704aa9a71f/stumme}, keywords = {introduction myown ml web ki ir 2007 mining} } @inproceedings{cumby02learningfdl, title = {Learning with Feature Description Logics}, author = {Chad M. Cumby and Dan Roth}, booktitle = {Proceedings of the 12th International Conference on Inductive Logic Programming (ILP 2002), July 9--11, 2002, Sydney, Australia --- Revised Papers}, editor = {Stan Matwin and Claude Sammut}, number = 2583, pages = {32--47}, publisher = {Springer, Berlin--Heidelberg, Germany}, series = {Lecture Notes in Computer Science}, year = 2003, biburl = {http://www.bibsonomy.org/bibtex/24d04bfd2f6789c757bfb2aa8191a6edb/sb3000}, keywords = {structured-data propositionalization ml} } @book{hastie01statisticallearning, title = {The Elements of Statistical Learning}, address = {New York, NY, USA}, author = {Trevor Hastie and Robert Tibshirani and Jerome Friedman}, publisher = {Springer New York Inc.}, series = {Springer Series in Statistics}, year = 2001, biburl = {http://www.bibsonomy.org/bibtex/2f58afc5c9793fcc8ad8389824e57984c/sb3000}, keywords = {ml statistics} } @book{mitchel97machinelearning, title = {Machine Learning}, address = {New York, NY, USA}, author = {Thomas Mitchell}, howpublished = {Paperback}, month = {October}, publisher = {McGraw-Hill}, year = 1997, url = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike-20\&path=ASIN/0071154671}, isbn = {0071154671}, biburl = {http://www.bibsonomy.org/bibtex/252d71136494540d7f5712edc0dc5b3dc/sb3000}, keywords = {ml} } @techreport{Mitchell2006, title = {The discipline of machine learning}, author = {Tom M. Mitchell}, institution = {Carnegie Mellon University - ML Department}, month = {July}, number = {CMU-ML-06-108}, year = 2006, url = {http://www.ml.cmu.edu/CMU-ML-06-108.pdf}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/28f5f1af12b759183d0316581dca8f9e7/marcoalvarez}, keywords = {ML} } @inproceedings{Caruana2006, title = {An empirical comparison of supervised learning algorithms}, author = {Rich Caruana and Alexandru Niculescu-Mizil}, booktitle = {International Conference on Machine Learning}, pages = {161--168}, year = 2006, url = {http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/2c4c598c0587dfb2b57f1ed9f0082484d/marcoalvarez}, keywords = {Classification ML} } @proceedings{2005-lws-proceedings, title = {Proceedings of the Workshop on Learning in Web Search (LWS 2005) }, editor = {Stephan Bloehdorn and Wray Buntine and Andreas Hotho}, note = {Workshop at the 22nd International Conference on Machine Learning (ICML 2005) }, year = 2005, url = {http://cosco.hiit.fi/search/learninginsearch05/ICML_W4.pdf}, biburl = {http://www.bibsonomy.org/bibtex/22de98c2b635f36c137e25256e8c235e0/sb3000}, keywords = {sb sb-editor ir ml} } @article{Lee1995d, title = {An EM-based approach for parameter enhancement with an application to speech signals}, author = {Byung-Gook Lee and Ki Yong Lee and Souguil Ann}, journal = {Signal Processing}, month = {Sep}, number = 1, pages = {1--14}, volume = 46, year = 1995, url = {http://www.sciencedirect.com/science/article/B6V18-3YXBCSB-R/1/efcdd0b58973e9ae88343c4b658153f7}, biburl = {http://www.bibsonomy.org/bibtex/2e7cac98b75f3399597b8bb9fac527341/smicha}, keywords = {ML estimation} } @inproceedings{anti2008krause, title = {The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems}, author = {Beate Krause and Andreas Hotho and Gerd Stumme}, booktitle = {Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web}, year = 2008, url = {http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf}, biburl = {http://www.bibsonomy.org/bibtex/203d349d70b578ca9ac3155f661151868/hotho}, keywords = {folksonomy mining dm myown ml social classification bookmarking spam 2008} } @book{lloyd03logicforlearning, title = {Logic for Learning: Learning Comprehensible Theories from Structured Data}, address = {Berlin--Heidelberg, Germany}, author = {J.W. Lloyd}, publisher = {Springer}, year = 2003, biburl = {http://www.bibsonomy.org/bibtex/2502a6bf8f280f1c850a46cece43cc7a7/sb3000}, keywords = {ml logic} }