"Committee-Based Sampling For Training Probabilistic Classifiers"

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Book	Author(s)	Year	Publisher
Probabilistic modeling framework for assessing water quality sampling programs	Vanessa Speight	2009	Water Research Foundation
Multimedia-based instructional design: computer-based training, Web-based training, distance broadcast training	William W. Lee; Diana L. Owens	2000	Jossey-Bass/Pfeiffer
Multimedia-based instructional design: computer-based training, web-based training, distance broadcast training, performance-based solutions	William W Lee; William W. Lee; Diana L. Owens	2004	Jossey-Bass
Evidence-based training methods: a guide for training professionals	Ruth Colvin Clark	2010	ASTD Press
Training the trainer: performance-based training for today's workplace
Technology-Based Training	M Labinger; P J Finch	1986	Pergamon
Technology Based Training	Serge Ravet; Maureen Layte	October 1, 1998	Kogan Page Ltd
Web-based training	Colin Steed	1999	Gower
Web-based training	Badrul Huda Khan	2001	Educational Technology Publications
Stopping rules for class of sampling-based stochastic programming algorithms	David P. Morton	1994	Naval Postgraduate School;Available from National Technical Information Service
Code for Classifiers Principles Governing the Cons	W. S. Merrill	January 2000	Amer Library Assn Editions
The Business Case for Web-Based Training	Tammy Whalen; David Wright (undifferentiated)	June 2000	Artech House Publishers
Authoring-systems software for computer-based training	William D. Milheim	1994	Educational Technology Publications
Handbook for developing competency-based training programs	William E. Blank	1982	Prentice-Hall
A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis	Heike Hufnagel	2011	Vieweg+Teubner Verlag
Web-based training cookbook	Brandon Hall	1997	Wiley
Designing competence-based training	Shirley Fletcher	1991	Kogan Page in association with the Institute of Training and Development
Probabilistic Methods for Bioinformatics	Richard E. Neapolitan	2009	Morgan Kaufmann
Sampling Essentials: Practical Guidelines for Making Sampling Choices	Johnnie Daniel	May 2011	SAGE Publications, Inc
A non-random sampling method, based on congruences, for "Monte Carlo" problems.	Robert D. Richtmyer	1958	Courant Institute of Mathematical Sciences, New York University
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Showing 1 to 20 of 100 entries

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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Committee-Based Sampling For Training Probabilistic Classifiers	Ido Dagan; Sean P. Engelson	Machine Learning Proceedings 1995	Armand Prieditis and Stuart J. Russell	1995	Morgan Kaufmann
Probabilistic classifiers		Machine Learning in Java	AshishSingh Bhatia, Bostjan Kaluza	2018-11-28	Packt Publishing Ltd
Understanding Probabilistic Classifiers	Ashutosh Garg; Dan Roth	Machine Learning: ECML 2001: 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001. Proceedings	Luc Raedt; Peter Flach	2001	Springer Berlin Heidelberg
Theory: Probabilistic Classifiers		Machine Learning in Computer Vision	Nicu Sebe; Ira Cohen; Ashutosh Garg; Thomas S. Huang	2005	Springer Netherlands
8 - Probabilistic Classifiers		Data-Driven Computational Neuroscience: Machine Learning and Statistical Models	Concha Bielza; Pedro Larrañaga	2020-11	Cambridge University Press
Training Classifiers		Natural Language Processing and Machine Learning for Developers	Oswald Campesato	2021	Mercury Learning and Information
Training Classifiers		Angular and Machine Learning Pocket Primer	Oswald Campesato	2020	Mercury Learning & Information
Probabilistic sampling		Mastering Distributed Tracing: Analyzing performance in microservices and complex systems	Yuri Shkuro	2019	Packt Publishing
Probabilistic sampling:		Encyclopedia of Forest Sciences	Burley J. (ed.)	2004	Elsevier, Academic Press
Probabilistic Sampling		Encyclopedic Dictionary of Archaeology	Barbara Ann Kipfer	2nd ed. 2021	Springer International Publishing
Combining Rule-Based and Sample-Based Classifiers – Probabilistic Approach	Marek Kurzynski	Brain, Vision, and Artificial Intelligence: First International Symposium, BVAI 2005, Naples, Italy, October 19-21, 2005, Proceedings	Massimo Gregorio; Vito Maio; Maria Frucci; Carlo Musio	2005	Springer Berlin Heidelberg
Combining Rule-Based and Sample-Based Classifiers – Probabilistic Approa		Brain, Vision, and Artificial Intelligence: First International Symposium, BVAI 2005, Naples, Italy, October 19 – 21, 2005. Proceedings	Péter Érdi, János Tóth (auth.), Massimo De Gregorio, Vito Di Maio, Maria Frucci, Carlo Musio (eds.)	2005	Springer-Verlag Berlin Heidelberg
7 - Non-probabilistic Classifiers		Data-Driven Computational Neuroscience: Machine Learning and Statistical Models	Concha Bielza; Pedro Larrañaga	2020-11	Cambridge University Press
Training the Classifiers	Jan Drugowitsch	Design and Analysis of Learning Classifier Systems: A Probabilistic Approach	Jan Drugowitsch	2008	Springer Berlin Heidelberg
Training Image Classifiers		Building interactive systems: principles for human-computer interaction	Dan R . Olsen, Jr	2009	Cengage Learning
Probabilistic sampling methods		Quantitative Methods for Business & Management	John Buglear	2011	Pearson
Non-probabilistic Sampling		Encyclopedic Dictionary of Archaeology	Barbara Ann Kipfer	2nd ed. 2021	Springer International Publishing
12. Probabilistic Sampling	Jeffrey M. Wooldridge	Handbook of Probability: Theory and Applications	Tamás Rudas	February 2008	SAGE Publications, Inc
Fusion of Rule-Based and Sample-Based Classifiers – Probabilistic Approach	Marek Kurzynski	Computer and Information Sciences - ISCIS 2005: 20th International Symposium, Istanbul, Turkey, October 26 -- 28, 2005, Proceedings	Pinar Yolum; Tunga Güngör; Fikret Gürgen; Can Özturan	2005	Springer Berlin Heidelberg
Manifold Learning-based Data Sampling for Model Training	Shuqing Chen; Sabrina Dorn; Michael Lell; Marc Kachelrieß; Andreas Maier	Bildverarbeitung für die Medizin 2018: Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen	Andreas Maier; Thomas M. Deserno; Heinz Handels; Klaus Hermann Maier-Hein; Christoph Palm; Thomas Tolxdorff	1. Aufl. 2018	Springer Berlin Heidelberg
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