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Recent advances on machine learning and Cybernetics

Witold Pedrycz, Daniel Yeung, Xizhao Wang
2010 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
We would like to thank the Editor-in-Chief of SOFT COM-PUTING for the encouragement, support and guidance during the realization of this special issue. Guest Editors  ...  The Guest Editors issue would like to take this opportunity and express their sincere thanks to the reviewers for their detailed comments as well as constructive suggestions on how to improve the quality  ...  Several contributions elaborate on realworld applications of machine learning and cybernetics to medical image processing and business intelligence.  ... 
doi:10.1007/s00500-010-0590-2 fatcat:swmdjebi7nhrrfzwg6bl3obb4u

International journal of machine learning and cybernetics

Xi-Zhao Wang
2010 International Journal of Machine Learning and Cybernetics  
The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid  ...  New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC.  ... 
doi:10.1007/s13042-010-0010-z fatcat:voofi5zkljgbte4d5mnng4zd2e

A machine learning approach to recognizing acronyms and their expansion

Jun Xu, Ya-Lou Huang
2005 2005 International Conference on Machine Learning and Cybernetics  
The study presents a simplified feature extraction process from spatial and temporal traits of the event feeds that is further subjected to the machine learning mechanism for boosting recognition performance  ...  With the wide ranges of real-time event feed capturing devices, there has been significant progress in the area of digital image processing towards activity detection and recognition.  ...  There are various justifications of using machine-based learning approach.  ... 
doi:10.1109/icmlc.2005.1527330 fatcat:fglaeyg7u5g2folx75xfbi6ldu

Program Committee

2020 2020 International Conference on Machine Learning and Cybernetics (ICMLC)  
International Conference on Machine Learning and Cybernetics (ICMLC) | 978-1-6654-1943-7/20/$31.00 ©2020 IEEE | DOI: 10.1109/ICMLC51923.2020.9469555  ... 
doi:10.1109/icmlc51923.2020.9469555 fatcat:jzbcbhx5ibg73jdcegxnkk26aa

Parallel classifiers ensemble with hierarchical machine learning for imbalanced classes

Yun Zhang, Bing Luo
2008 2008 International Conference on Machine Learning and Cybernetics  
Experimental results in machine vision quality inspection showed that the approach could effectively improve classification speed and decrease total risk for imbalanced classes' classification.  ...  Most samples would be correctly recognized by the first classifier, and the second relatively slower classifier could be ended. The second one was only trained and worked for less difficult samples.  ...  So some practicable indexes must be transformed from expression (1) for machine learning and evaluation.  ... 
doi:10.1109/icmlc.2008.4620385 fatcat:2al56wnq4jby5ckr3xz3j7s3k4

Contrast Learning for Conceptual Proximity Matching

L. Massey
2007 2007 International Conference on Machine Learning and Cybernetics  
The approach is based on the idea that processing example cases that contrast with existing knowledge but are conceptually close provide a learning opportunity.  ...  We present experimental results with a set of real life examples and demonstrate that the newly acquired knowledge facilitates processing of novel cases.  ...  This is a common sense rule that was learned from the contrast between terms live and move present respectively in the KB and in an example case.  ... 
doi:10.1109/icmlc.2007.4370853 fatcat:n7kggqqxfrbcjfbinjiuyzhq6m

Editorial

Christos Anagnostopoulos, Kostas Kolomvatsos
2015 International Journal of Machine Learning and Cybernetics  
The adoption of Machine Learning (ML) and/or Computational Intelligence (CI) in handling big data could offer a number of advantages.  ...  Drifts are detected by comparing two accuracies: (i) the accuracy of an ensemble on the recent examples, and (ii) the accuracy from the beginning of the learning process.  ...  The adoption of Machine Learning (ML) and/or Computational Intelligence (CI) in handling big data could offer a number of advantages.  ... 
doi:10.1007/s13042-015-0429-3 fatcat:6ml6qxnwxvb33mbdvpu67rooey

List of Reviewers

2020 2020 International Conference on Machine Learning and Cybernetics (ICMLC)  
International Conference on Machine Learning and Cybernetics (ICMLC) | 978-1-6654-1943-7/20/$31.00 ©2020 IEEE | DOI: 10.1109/ICMLC51923.2020.9469559  ... 
doi:10.1109/icmlc51923.2020.9469559 fatcat:l4zfp4xqjjbztc2o4avdrnewfi

Applying machine learning techniques in detecting Bacterial Vaginosis

Yolanda S. Baker, Rajeev Agrawal, James A. Foster, Daniel Beck, Gerry Dozier
2014 2014 International Conference on Machine Learning and Cybernetics  
We isolated the clinical and medical features from the full set of raw data, we compared the accuracy, precision, recall and F-measure and time elapsed for each feature selection and classification grouping  ...  This paper seeks to uncover the most important features for diagnosis and in turn employ classification algorithms on those features.  ...  The author would like to thank the NSF, BEACON and NIH for their support of this research.  ... 
doi:10.1109/icmlc.2014.7009123 pmid:25914861 pmcid:PMC4407517 dblp:conf/icmlc/BakerAFBD14 fatcat:hwceyh4ah5a6pnchpsxpmsddtm

Incremental extreme learning machine based on deep feature embedded

Jian Zhang, Shifei Ding, Nan Zhang, Zhongzhi Shi
2015 International Journal of Machine Learning and Cybernetics  
To solve the problems above, we summarize the features of extreme learning machine and deep belief networks, and then propose Incremental extreme learning machine based on Deep Feature Embedded algorithm  ...  Extreme learning machine (ELM) algorithm is used to train Single-hidden Layer Feed forward Neural Networks. And Deep Belief Network (DBN) is based on Restricted Boltzmann Machine (RBM).  ...  extreme learning machine [9] .  ... 
doi:10.1007/s13042-015-0419-5 fatcat:pl445zsspvf2bepxezvxza352y

Learning ontology from relational database

Man Li, Xiao-Yong Du, Shan Wang
2005 2005 International Conference on Machine Learning and Cybernetics  
However, building ontology by hand is a very hard and error-prone task. Learning ontology from existing resources is a good solution.  ...  Because relational database is widely used for storing data and OWL is the latest standard recommended by W3C, this paper proposes an approach of learning OWL ontology from data in relational database.  ...  Acknowledgements The work was supported by the National Natural Science Foundation of China (Grant No. 60496325) and "211" project.  ... 
doi:10.1109/icmlc.2005.1527531 fatcat:rj4b32xlmbgetbomjyfjwfu5km

Greetings from General Chairs

2020 2020 International Conference on Machine Learning and Cybernetics (ICMLC)  
Peng Shi and Cheng-Chew Lim General Chairs, ICMLC International Conference on Machine Learning and Cybernetics (ICMLC) | 978-1-6654-1943-7/20/$31.00 ©2020 IEEE | DOI: 10.1109/ICMLC51923.2020.9469552  ...  Virtual conferences at ICMLC will also show state-of-the-art technologies, ideas, and solutions.  ... 
doi:10.1109/icmlc51923.2020.9469552 fatcat:4pqx36mqordnfcntm2aknpyolm

Clustering ensemble method

Tahani Alqurashi, Wenjia Wang
2018 International Journal of Machine Learning and Cybernetics  
It employs two similarity measures, cluster similarity and a newly defined membership similarity, and works adaptively through three stages.  ...  Our proposed method is tested on various real-world benchmark datasets and its performance is compared with other state-of-the-art clustering ensemble methods, including the Co-association method and the  ...  Introduction In the context of machine learning, an ensemble is generally defined as "a machine learning system that is constructed with a set of individual models working in parallel, whose outputs are  ... 
doi:10.1007/s13042-017-0756-7 fatcat:meaa6qmp3nfa7honl5pdyoa27e

Applications for Machine Learning [Editorial]

Saeid Nahavandi
2021 IEEE Systems Man and Cybernetics Magazine  
In this issue of IEEE Systems, Man, and Cybernetics Magazine, we present four articles related to the fascinating topic of machine learning and its application for real-world systems.  ...  It is argued that novel machine learning techniques have excellent accuracy but poor explainability and interpretability.  ... 
doi:10.1109/msmc.2021.3058718 fatcat:5tcc264mgvcn3l6qioup2ba65a

Gained Knowledge Exchange and Analysis for Meta-Learning

Norbert Jankowski, Krzysztof Grabczewski
2007 2007 International Conference on Machine Learning and Cybernetics  
learning processes and their results.  ...  Efficient meta-learning is possible only within a versatile and flexible data mining framework providing uniform procedures for dealing with different kinds of methods and tools for thorough analysis of  ...  , the term learning machine (learning method, shortly machine) is used to describe an adaptive algorithm.  ... 
doi:10.1109/icmlc.2007.4370251 fatcat:2drg4bxrgfbrxopnw7qm77buje
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