"Improving Representation of the Positive Class in Imbalanced Multiple-Instance Learning"

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Book	Author(s)	Year	Publisher
Multiple Instance Learning: Foundations and Algorithms	Francisco Herrera; Sebastián Ventura; Rafael Bello; Chris Cornelis; Amelia Zafra; Dánel Sánchez-Tarragó; Sarah Vluymans	1st ed. 2016	Springer International Publishing
Learning from Imbalanced Data Sets	Alberto Fernández; Salvador García; Mikel Galar; Ronaldo C. Prati; Bartosz Krawczyk; Francisco Herrera	1st ed. 2018	Springer International Publishing
Imbalanced Learning: Foundations, Algorithms, and Applications	Haibo He; Yunqian Ma	2013	Wiley-IEEE Press
Positive Teaching, Positive Learning	Rob Barnes	1999	Routledge
Improving writing and learning: a handbook for teachers in every class	Judith Bechtel	1985	Allyn and Bacon
Improving reading in every class	Ellen Lamar Thomas	1977	Allyn and Bacon
Improving learning in Uganda	Innocent Mulindwa Najjumba	2013	The World Bank
Representation of Multiple-Valued Logic Functions	Radomir S. Stankovic, Jaako T. Astola, Claudio Moraga	2012	Morgan & Claypool Publishers
Expressions: multiple intelligences in the English class	Peter Smagorinsky	1991	National Council of Teachers of English
Improving the learning of mathematics	Cedric Cullingford	1992	Heinemann
Multiple Sclerosis (Class Health)	Rosalind Kalb; Rosalind C., Phd Kalb; Rosalind C. Kalb	November 1, 2004	Class Publishing
Positive Unlabeled Learning	Kristen Jaskie; Andreas Spanias		Springer International Publishing
Positive Unlabeled Learning	Jaskie Spanias	2022	Synthesis Lectures on Artifici
Classroom Karma: Positive Teaching, Positive Behaviour, Positive Learning	David Wright	2006	Routledge
Learning in the nursery class	Eva B. McCallum	1944	The Bethany press
Improving Learning in Later Life	Alexandra Withnall	2009	Routledge
Improving Learning in Secondary English	Geoff Dean	2004	Routledge
Improving learning transfer in organizations	Elwood F. Holton; Timothy T. Baldwin	2003	Jossey-Bass
A positive church in a negative world: learning and improving leadership in every experience of your church	Jonas Arrais	2007	Ministerial Association Resource Center, General Conference of Seventh-day Adventists
COPING WITH MULTIPLE SCLEROSIS (POSITIVE HEALTH)	CYNTHIA BENZ	1996	VERMILION
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Improving Representation of the Positive Class in Imbalanced Multiple-Instance Learning	Carlos Mera; Mauricio Orozco-Alzate; John Branch	Image Analysis and Recognition: 11th International Conference, ICIAR 2014, Vilamoura, Portugal, October 22-24, 2014, Proceedings, Part I	Aurélio Campilho; Mohamed Kamel	2014	Springer International Publishing
Multiple-Instance Learning	Soumya Ray; Stephen Scott; Hendrik Blockeel	Encyclopedia of Machine Learning and Data Mining	Claude Sammut; Geoffrey I. Webb	2nd ed. 2017	Springer US
Multiple Instance Learning	Francisco Herrera; Sebastián Ventura; Rafael Bello; Chris Cornelis; Amelia Zafra; Dánel Sánchez-Tarragó et al.	Multiple Instance Learning: Foundations and Algorithms	Francisco Herrera; Sebastián Ventura; Rafael Bello; Chris Cornelis; Amelia Zafra; Dánel Sánchez-Tarragó; Sarah Vluymans	1st ed. 2016	Springer International Publishing
Multiple-Instance Learning		Encyclopedia of Machine Learning	Claude Sammut; Geoffrey I. Webb	2010	Springer US
Multiple-Instance Learning	Soumya Ray; Stephen Scott; Hendrik Blockeel	Encyclopedia of Machine Learning and Data Science	Dinh Phung; Geoffrey I. Webb; Claude Sammut	0th ed. 2020	Springer US
Improving Promoter Prediction Using Multiple Instance Learning	P. J. Uren; R. M. Cameron-Jones; A. H. J. Sale	AI 2008: Advances in Artificial Intelligence: 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 3-5, 2008, Proceedings	Wayne Wobcke; Mengjie Zhang	2008	Springer Berlin Heidelberg
Multiple Instance Multiple Label Learning	Francisco Herrera; Sebastián Ventura; Rafael Bello; Chris Cornelis; Amelia Zafra; Dánel Sánchez-Tarragó et al.	Multiple Instance Learning: Foundations and Algorithms	Francisco Herrera; Sebastián Ventura; Rafael Bello; Chris Cornelis; Amelia Zafra; Dánel Sánchez-Tarragó; Sarah Vluymans	1st ed. 2016	Springer International Publishing
Unsupervised Multiple Instance Learning	Francisco Herrera; Sebastián Ventura; Rafael Bello; Chris Cornelis; Amelia Zafra; Dánel Sánchez-Tarragó et al.	Multiple Instance Learning: Foundations and Algorithms	Francisco Herrera; Sebastián Ventura; Rafael Bello; Chris Cornelis; Amelia Zafra; Dánel Sánchez-Tarragó; Sarah Vluymans	1st ed. 2016	Springer International Publishing
Class-Dependent Dissimilarity Measures for Multiple Instance Learning	Veronika Cheplygina; David M. J. Tax; Marco Loog	Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2012, Hiroshima, Japan, November 7-9, 2012, Proceedings	Georgy Gimel´farb; Edwin Hancock; Atsushi Imiya; Arjan Kuijper; Mineichi Kudo; Shinichiro Omachi; Terry Windeatt; Keiji Yamada	2012	Springer Berlin Heidelberg
Improving Multiple-Instance Learning via Disambiguation by Considering Generalization	Lu Zhao; Youjian Yu; Hao Chen; Liming Yuan	Wireless Internet: 10th International Conference, WiCON 2017, Tianjin, China, December 16-17, 2017, Proceedings	Cheng Li; Shiwen Mao	1st ed. 2018	Springer International Publishing
Label Stability in Multiple Instance Learning	Veronika Cheplygina; Lauge Sørensen; David M. J. Tax; Marleen de Bruijne; Marco Loog	Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I	Nassir Navab; Joachim Hornegger; William M. Wells; Alejandro Frangi	1st ed. 2015	Springer International Publishing
Improving Bagging Ensembles for Class Imbalanced Data by Active Learning	Jerzy Błaszczyński; Jerzy Stefanowski	Advances in Feature Selection for Data and Pattern Recognition	Urszula Stańczyk; Beata Zielosko; Lakhmi C. Jain	1st ed. 2018	Springer International Publishing
Multiple-Instance Learning with Evolutionary Instance Selection	Yongshan Zhang; Jia Wu; Chuan Zhou; Peng Zhang; Zhihua Cai	Database Systems for Advanced Applications: 21st International Conference, DASFAA 2016, Dallas, TX, USA, April 16-19, 2016, Proceedings, Part I	Shamkant B. Navathe; Weili Wu; Shashi Shekhar; Xiaoyong Du; X. Sean Wang; Hui Xiong	1st ed. 2016	Springer International Publishing
Salient Instance Selection for Multiple-Instance Learning	Liming Yuan; Songbo Liu; Qingcheng Huang; Jiafeng Liu; Xianglong Tang	Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part III	Tingwen Huang; Zhigang Zeng; Chuandong Li; Chi-Sing Leung	2012	Springer Berlin Heidelberg
Imbalanced Multi-instance Data	Francisco Herrera; Sebastián Ventura; Rafael Bello; Chris Cornelis; Amelia Zafra; Dánel Sánchez-Tarragó et al.	Multiple Instance Learning: Foundations and Algorithms	Francisco Herrera; Sebastián Ventura; Rafael Bello; Chris Cornelis; Amelia Zafra; Dánel Sánchez-Tarragó; Sarah Vluymans	1st ed. 2016	Springer International Publishing
Evaluating Multi-class Multiple-Instance Learning for Image Categorizati		Computer Vision – ACCV 2007: 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part II	Yufei Han, Zhenan Sun, Fei Wang, Tieniu Tan (auth.), Yasushi Yagi, Sing Bing Kang, In So Kweon, Hongbin Zha (eds.)	2007	Springer-Verlag Berlin Heidelberg
Evaluating Multi-class Multiple-Instance Learning for Image Categorization	Xinyu Xu; Baoxin Li	Computer Vision - ACCV 2007: 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part II	Yasushi Yagi; Sing Bing Kang; In So Kweon; Hongbin Zha	2007	Springer Berlin Heidelberg
Dissimilarity-Based Multiple Instance Learning	Lauge Sørensen; Marco Loog; David M. J. Tax; Wan-Jui Lee; Marleen de Bruijne; Robert P. W. Duin	Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings	Edwin R. Hancock; Richard C Wilson; Terry Windeatt; Ilkay Ulusoy; Francisco Escolano	2010	Springer Berlin Heidelberg
Imbalanced learning		Hands-On One-shot Learning with Python: Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch	Shruti Jadon, Ankush Garg	2020	Packt Publishing
Imbalanced Learning		Self-Adaptive Systems for Machine Intelligence	Haibo He	2011	John Wiley & Sons
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Showing 1 to 20 of 100 entries

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