Unsupervised learning

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In machine learning, unsupervised learning refers to the problem of trying to find hidden structure in unlabeled data. (Wikipedia.org)




IEEE Organizations related to Unsupervised learning

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Conferences related to Unsupervised learning

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2013 International Conference on Machine Learning and Cybernetics (ICMLC)

Statistical Machine Learning, Intelligent & fuzzy control, Pattern Recognition , Ensemble method, Evolutionary computation, Fuzzy & rough set, Data & web mining , Intelligent Business Computing , Biometrics , Bioinformatics , Information retrieval, Cybersecurity, Web intelligence and technology, Semantics & ontology engineering, Social Networks & Ubiquitous Intelligence, Multicriteria decision making, Soft Computing, Intelligent Systems, Speech, Image & Video Processing, Decision Support System

  • 2012 International Conference on Machine Learning and Cybernetics (ICMLC)

    Adaptive systems, Pattern Recognition, Biometrics, Inductive learning, Evolutionary computation, Bioinformatics, Data mining, Information retrieval, Intelligent agent, Financial engineering, Rough Set, Applications.

  • 2011 International Conference on Machine Learning and Cybernetics (ICMLC)

    Adaptive systems, Neural net and support vector machine, Business intelligence, Hybrid and nonlinear system, Biometrics, Fuzzy set theory, fuzzy control and system, Bioinformatics, Knowledge management, Data and web mining, Information retrieval, Intelligent agent, Intelligent and knowledge based system, Financial engineering, Rough and fuzzy rough set, Inductive learning, Networking and information security, Geoinformatics, Evolutionary computation, Pattern Recognition, Ensemble method, Logistics.

  • 2010 International Conference on Machine Learning and Cybernetics (ICMLC)

    Adaptive systems, Neural net and support vector machine, Business intelligence, Hybrid and nonlinear system, Biometrics, Fuzzy set theory, fuzzy control and system, Bioinformatics, Knowledge management, Data and web mining, Information retrieval, Intelligent agent, Intelligent and knowledge based system, Financial engineering, Rough and fuzzy rough set, Inductive learning, Networking and information security, Geoinformatics, Evolutionary computation, Pattern Recognition, Ensemble method, Logistics, Informat

  • 2009 International Conference on Machine Learning and Cybernetics (ICMLC)

    Adaptive systems, Neural net and support vector machine, Business intelligence, Hybrid and nonlinear system, Biometrics, Fuzzy set theory, fuzzy control and system, Bioinformatics, Knowledge management, Data and web mining, Information retrieval, Intelligent agent, Intelligent and knowledge based system, Financial engineering, Rough and fuzzy rough set, Inductive learning, Networking and information security, Geoinformatics, Evolutionary computation, Pattern Recognition, Ensemble method, Logistics, Informat

  • 2008 International Conference on Machine Learning and Cybernetics (ICMLC)

    Adaptive systems, Neural net and support vector machine, Business intelligence, Hybrid and nonlinear system, Biometrics, Fuzzy set theory, fuzzy control and system, Bioinformatics, Knowledge management, Data and web mining, Information retrieval, Intelligent agent, Intelligent and knowledge based system, Financial engineering, Rough and fuzzy rough set, Inductive learning, Networking and information security, Geoinformatics, Evolutionary computation, Pattern Recognition, Ensemble method, Logistics, Informat

  • 2007 International Conference on Machine Learning and Cybernetics (ICMLC)

    Multiple themes included: Generalization Error Model for Pattern Classification, Rough Sets and Fuzzy Rough Sets, Multiple Classifier Systems, Computation Life Science and Bioinformatics, Media Computing, Web Intelligent Computing. Topics included: Adaptive systems, Neural nets and support vector machines, Business intelligence, Hybrid and nonlinear systems, Fuzzy theory, control and systems, Data and web mining, Information retrieval, intelligent agent etc.

  • 2006 International Conference on Machine Learning and Cybernetics (ICMLC)

  • 2005 International Conference on Machine Learning and Cybernetics (ICMLC)


2012 10th World Congress on Intelligent Control and Automation (WCICA 2012)

A. Intelligent Control B. Control Theory and Control Engineering C. Complex Systems and Intelligent Robots D. Others


2012 46th Annual Conference on Information Sciences and Systems (CISS)

Theoretical advances, applications and ideas in the fields of information theory (including application to biological sciences); communication, networking, signal, image and video processing; systems and control; learning and statistical inference.


2012 IEEE 15th International Conference on Computational Science and Engineering (CSE)

The Computational Science and Engineering area has earned prominence through advances in electronic and integrated technologies beginning in the 1940s. Current times are very exciting and the years to come will witness a proliferation in the use of various advanced computing systems. It is increasingly becoming an emerging and promising discipline in shaping future research and development activities in academia and industry, ranging from engineering, science, finance, economics, arts and humanitarian fields, especially when the solution of large and complex problems must cope with tight timing schedules.



Periodicals related to Unsupervised learning

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Knowledge and Data Engineering, IEEE Transactions on

Artificial intelligence techniques, including speech, voice, graphics, images, and documents; knowledge and data engineering tools and techniques; parallel and distributed processing; real-time distributed processing; system architectures, integration, and modeling; database design, modeling, and management; query design, and implementation languages; distributed database control; statistical databases; algorithms for data and knowledge management; performance evaluation of algorithms and systems; data communications aspects; system ...




Xplore Articles related to Unsupervised learning

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An Autonomous Diagnostics and Prognostics Framework for Condition-Based Maintenance

Baruah, P.; Chinnam, R.B.; Filev, D. Neural Networks, 2006. IJCNN '06. International Joint Conference on, 2006

This paper presents an innovative on-line approach for autonomous diagnostics and prognostics. It overcomes limitations of current diagnostics and prognostics technology by developing a "generic" framework that is relatively independent of the type of physical equipment under consideration. Proposed diagnostics and prognostics framework (DPF) is based on unsupervised learning methods (reducing the need for human intervention). The procedures used in ...


A Feature Selection Method Using Weighted Clusterer Ensemble

Shuchu Xiong; Yihui Luo Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on, 2009

Processing applications with a large number of dimensions has been a challenge to the data mining community. Feature selection is an effective dimensionality reduction technique. However, there are only a few methods proposed for feature selection for clustering. In this paper, a new feature selection algorithm for unsupervised learning is introduced. It is based on the assumption that, in absence ...


On rectified linear units for speech processing

Zeiler, M.D.; Ranzato, M.; Monga, R.; Mao, M.; Yang, K.; Le, Q.V.; Nguyen, P.; Senior, A.; Vanhoucke, V.; Dean, J.; Hinton, G.E. Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, 2013

Deep neural networks have recently become the gold standard for acoustic modeling in speech recognition systems. The key computational unit of a deep network is a linear projection followed by a point-wise non-linearity, which is typically a logistic function. In this work, we show that we can improve generalization and make training of deep networks faster and simpler by substituting ...


Low-resource speech translation of Urdu to English using semi-supervised part-of-speech tagging and transliteration

Ryan Aminzadeh, A.; Shen, W. Spoken Language Technology Workshop, 2008. SLT 2008. IEEE, 2008

This paper describes the construction of ASR and MT systems for translation of speech from Urdu into English. As both Urdu pronunciation lexicons and Urdu- English bitexts are sparse, we employ several techniques that make use of semi-supervised annotation to improve ASR and MT training. Specifically, we describe 1) the construction of a semi-supervised HMM-based part-of-speech tagger that is used ...


Robust Vehicle Detection for Tracking in Highway Surveillance Videos Using Unsupervised Learning

Tamersoy, B.; Aggarwal, J.K. Advanced Video and Signal Based Surveillance, 2009. AVSS '09. Sixth IEEE International Conference on, 2009

This paper presents a novel approach to vehicle detection in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to form an unsupervised system, where vehicles are automatically ldquolearnedrdquo from video sequences. First an enhanced adaptive background mixture model is used to identify positive and negative examples. Then a classifier is trained with these examples. In ...


More Xplore Articles

Educational Resources on Unsupervised learning

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eLearning

An Autonomous Diagnostics and Prognostics Framework for Condition-Based Maintenance

Baruah, P.; Chinnam, R.B.; Filev, D. Neural Networks, 2006. IJCNN '06. International Joint Conference on, 2006

This paper presents an innovative on-line approach for autonomous diagnostics and prognostics. It overcomes limitations of current diagnostics and prognostics technology by developing a "generic" framework that is relatively independent of the type of physical equipment under consideration. Proposed diagnostics and prognostics framework (DPF) is based on unsupervised learning methods (reducing the need for human intervention). The procedures used in ...


A Feature Selection Method Using Weighted Clusterer Ensemble

Shuchu Xiong; Yihui Luo Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on, 2009

Processing applications with a large number of dimensions has been a challenge to the data mining community. Feature selection is an effective dimensionality reduction technique. However, there are only a few methods proposed for feature selection for clustering. In this paper, a new feature selection algorithm for unsupervised learning is introduced. It is based on the assumption that, in absence ...


On rectified linear units for speech processing

Zeiler, M.D.; Ranzato, M.; Monga, R.; Mao, M.; Yang, K.; Le, Q.V.; Nguyen, P.; Senior, A.; Vanhoucke, V.; Dean, J.; Hinton, G.E. Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, 2013

Deep neural networks have recently become the gold standard for acoustic modeling in speech recognition systems. The key computational unit of a deep network is a linear projection followed by a point-wise non-linearity, which is typically a logistic function. In this work, we show that we can improve generalization and make training of deep networks faster and simpler by substituting ...


Low-resource speech translation of Urdu to English using semi-supervised part-of-speech tagging and transliteration

Ryan Aminzadeh, A.; Shen, W. Spoken Language Technology Workshop, 2008. SLT 2008. IEEE, 2008

This paper describes the construction of ASR and MT systems for translation of speech from Urdu into English. As both Urdu pronunciation lexicons and Urdu- English bitexts are sparse, we employ several techniques that make use of semi-supervised annotation to improve ASR and MT training. Specifically, we describe 1) the construction of a semi-supervised HMM-based part-of-speech tagger that is used ...


Robust Vehicle Detection for Tracking in Highway Surveillance Videos Using Unsupervised Learning

Tamersoy, B.; Aggarwal, J.K. Advanced Video and Signal Based Surveillance, 2009. AVSS '09. Sixth IEEE International Conference on, 2009

This paper presents a novel approach to vehicle detection in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to form an unsupervised system, where vehicles are automatically ldquolearnedrdquo from video sequences. First an enhanced adaptive background mixture model is used to identify positive and negative examples. Then a classifier is trained with these examples. In ...


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