"Semi-supervised classification of texts using particle learning for probabilistic automata"

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Book	Author(s)	Year	Publisher
Semi-Supervised Learning	Olivier Chapelle; Bernhard Schölkopf; Alexander Zien	2006	MIT Press
Semi-Supervised Learning	Olivier Chapelle; Bernhard Scholkopf; Alexander Zien	2006	The MIT Press
Semi-supervised learning	Olivier Chapelle; Bernhard Schölkopf; Alexander Zien	2010	MIT Press
Introduction to Semi-Supervised Learning	Xiaojin Zhu; Andrew. B Goldberg		Springer International Publishing
Graph-Based Semi-Supervised Learning	Amarnag Subramanya; Partha Pratim Talukdar		Springer International Publishing
Introduction to semi-supervised learning	Zhu X., Goldberg A.B	2009	Morgan
Graph-Based Semi-Supervised Learning	Amarnag Subramanya, Partha Pratim Talukdar	2014	Morgan & Claypool Publishers
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning	Te-Ming Huang; Vojislav Kecman; Ivica Kopriva	2006	Springer Berlin Heidelberg
Semi-Supervised and Unsupervised Machine Learning: Novel Strategies	Amparo Albalate	2011	John Wiley & Sons
Semi-Supervised Dependency Parsing	Wenliang Chen; Min Zhang	1st ed. 2015	Springer Nature Singapore
Introduction to Probabilistic Automata	Azaria Paz and Werner Rheinboldt	1971	Academic Press
Probabilistic languages and automata.	Clarence Arthur Ellis	1969	Dept. of Computer Science, University of Illinois
Particle classification /	Heiskanen, K. I.	1993.	Chapman & Hall,
Semi-Supervised Learning and Domain Adaptation in Natural Language Processing	Anders Søgaard		Springer International Publishing
Semi-Supervised Learning and Domain Adaptation in Natural Language Processing	Anders Sogaard	2013	Morgan & Claypool Publishers
Learning Automata	K. Najim and A. S. Poznyak	1994	Pergamon
Statistical Analysis Techniques in Particle Physics - Fits, Density Estimation and Supervised Learning	Ilya Narsky	2013	John Wiley & Sons
Fraud Analytics Using Supervised, Unsupervised and Social Network Learning Techniques	Bart Baesens	2015	John Wiley & Sons
Supervised Learning with Python: Concepts and Practical Implementation Using Python	Vaibhav Verdhan	1st ed.	Apress
Supervised and Unsupervised Learning for Data Science	Michael W. Berry; Azlinah Mohamed; Bee Wah Yap	1st ed. 2020	Springer International Publishing
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Semi-supervised classification of texts using particle learning for probabilistic automata	ANA PAULA SALES	Bayesian Theory and Applications	Paul Damien; Petros Dellaportas; Nicholas G. Polson; David A. Stephens	2013	Oxford University Press
Using Semi-supervised Learning for Question Classification	Nguyen Thanh Tri; Nguyen Minh Le; Akira Shimazu	Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead: 21st International Conference, ICCPOL 2006, Singapore, December 17-19, 2006, Proceedings	Yuji Matsumoto; Richard Sproat; Kam-Fai Wong; Min Zhang	2006	Springer Berlin Heidelberg
Semi-supervised Classification by Probabilistic Relaxation	Adolfo Martínez-Usó; Filiberto Pla; José Martínez Sotoca; Henry Anaya-Sánchez	Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 16th Iberoamerican Congress on Pattern Recognition, CIARP 2011, Pucón, Chile, November 15-18, 2011. Proceedings	César San Martin; Sang-Woon Kim	2011	Springer Berlin Heidelberg
PSSDL: Probabilistic Semi-supervised Dictionary Learning	Behnam Babagholami-Mohamadabadi; Ali Zarghami; Mohammadreza Zolfaghari; Mahdieh Soleymani Baghshah	Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III	Hendrik Blockeel; Kristian Kersting; Siegfried Nijssen; Filip Železný	2013	Springer Berlin Heidelberg
Using Clustering for Semi-Supervised Learning		Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition	Aurélien Géron	2019	O'Reilly
Using Clustering for Semi-Supervised Learning		Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow	Aurelien Geron	2019	O’reilly
Semi-supervised Metric Learning for Image Classification	Jiwei Hu; ChenSheng Sun; Kin Man Lam	Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings	Guoping Qiu; Kin Man Lam; Hitoshi Kiya; Xiang-Yang Xue; C.-C. Jay Kuo; Michael S. Lew	2010	Springer Berlin Heidelberg
Semi–supervised Learning for Image Modality Classification	Alba García Seco de Herrera; Dimitrios Markonis; Ranveer Joyseeree; Roger Schaer; Antonio Foncubierta-Rodríguez; Henning Müller	Multimodal Retrieval in the Medical Domain: First International Workshop, MRMD 2015, Vienna, Austria, March 29, 2015, Revised Selected Papers	Henning Müller; Oscar Alfonso Jimenez del Toro; Allan Hanbury; Georg Langs; Antonio Foncubierta Rodriguez	1st ed. 2015	Springer International Publishing
Self-supervised Learning for Semi-supervised Time Series Classification	Shayan Jawed; Josif Grabocka; Lars Schmidt-Thieme	Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, Proceedings, Part I	Hady W. Lauw; Raymond Chi-Wing Wong; Alexandros Ntoulas; Ee-Peng Lim; See-Kiong Ng; Sinno Jialin Pan	1st ed. 2020	Springer International Publishing
Semi-Supervised Classification		Encyclopedia of Database Systems	Ling Liu; M. Tamer Özsu	2009	Springer US
NLP2SQL Using Semi-supervised Learning	H. Vathsala; Shashidhar G. Koolagudi	Advanced Computing: 10th International Conference, IACC 2020, Panaji, Goa, India, December 5–6, 2020, Revised Selected Papers, Part I	Deepak Garg; Kit Wong; Jagannathan Sarangapani; Suneet Kumar Gupta	1st ed. 2021	Springer Nature Singapore
Probabilistic Learning – Classification Using		R: Data Analysis and Visualization	Edvin Moses	2016	Packt Publishing
Learning Probabilistic Automata Using Residuals	Wenjing Chu; Shuo Chen; Marcello Bonsangue	Theoretical Aspects of Computing – ICTAC 2021: 18th International Colloquium, Virtual Event, Nur-Sultan, Kazakhstan, September 8–10, 2021, Proceedings	Antonio Cerone; Peter Csaba Ölveczky	1st ed. 2021	Springer International Publishing
Semi-Supervised Learning Using Semi-Definite Programming	De Bie Tijl; Cristianini Nello	Semi-Supervised Learning	Olivier Chapelle; Bernhard Scholkopf; Alexander Zien	2006	The MIT Press
Semi-Supervised Learning Using Semi-Definite Programming		Semi-Supervised Learning	Olivier Chapelle; Bernhard Schölkopf; Alexander Zien	2006	MIT Press
Using a DCGAN for semi-supervised learning		TensorFlow 2. 0 Computer Vision Cookbook: Implement machine learning solutions to overcome various computer vision challenges	Jesús Martinez	2021	Packt Publishing
Semi-Supervised Text Classification Using EM	Nigam Kamal	Semi-Supervised Learning	Olivier Chapelle; Bernhard Scholkopf; Alexander Zien	2006	The MIT Press
Semi-Supervised Classification Using Pattern Clustering		Semi-Supervised and Unsupervised Machine Learning: Novel Strategies	Amparo Albalate	2011	John Wiley & Sons
Semi-Supervised Text Classification Using EM		Semi-Supervised Learning	Olivier Chapelle; Bernhard Schölkopf; Alexander Zien	2006	MIT Press
Semi-supervised Learning	Mohamed Farouk Abdel Hady; Friedhelm Schwenker	Handbook on Neural Information Processing	Monica Bianchini; Marco Maggini; Lakhmi C. Jain	2013	Springer Berlin Heidelberg
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Showing 1 to 20 of 100 entries

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