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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Actor-critic learning based on fuzzy inference system

Jouffe, L. Systems, Man, and Cybernetics, 1996., IEEE International Conference on, 1996

Actor-critic learning is a reinforcement learning method used to find an optimal agent behavior. The only information available for learning is the system feedback (reward/punishment). Initially, this method was analyzed for discrete states and actions. Functions were then approximated by lookup tables. Most of the real problems have large input spaces and/or continuous actions. So, other function approximators have to ...


Nonlinear mapping with minimal supervised learning

Tolat, V.V.; Peterson, A.M. System Sciences, 1990., Proceedings of the Twenty-Third Annual Hawaii International Conference on, 1990

The problem of interpolating unknown mappings from known mappings is addressed. This problem arises when a large number of mappings must be learned and it is impractical to train the network on all possible mappings. Described is a network model that can learn nonlinear mappings with a minimal amount of supervised training. A combination of supervised and supervised learning is ...


Realtime Audio to Score Alignment for Polyphonic Music Instruments, using Sparse Non-Negative Constraints and Hierarchical HMMS

Cont, A. Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, 2006

We present a new method for realtime alignment of audio to score for polyphonic music signals. In this paper, we will be focusing mostly on the multiple-pitch observation algorithm proposed based on realtime non-negative matrix factorization with sparseness constraints and hierarchical hidden Markov models for sequential modeling using particle filtering for decoding. The proposed algorithm has the advantage of having ...


Learning with a probabilistic teacher

Agrawala, A. Information Theory, IEEE Transactions on, 1970

The Bayesian learning scheme is computationally infeasible for most of the unsupervised learning problems. This paper suggests a learning scheme, "learning with a probabilistic teacher," which works with unclassified samples and is computationally feasible for many practical problems. In this scheme a sample is probabilistically assigned with a class with appropriate probabilities computed using all the information available: Then the ...


Boundary region sensitive classification for the counter-propagation neural network

Kovacs, L.; Terstyanszky, G. Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, 2000

The basic problem of classification priori unknown faults is related to re- arrangement of existing classes and/or introduction of new classes that requires management of uncertain regions where input pattern vectors may belong to several classes. The counter-propagation neural network (CPN) was selected to investigate the classification problems because it integrates both supervised and unsupervised learning to support diagnosis of ...


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Educational Resources on Unsupervised learning

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eLearning

Actor-critic learning based on fuzzy inference system

Jouffe, L. Systems, Man, and Cybernetics, 1996., IEEE International Conference on, 1996

Actor-critic learning is a reinforcement learning method used to find an optimal agent behavior. The only information available for learning is the system feedback (reward/punishment). Initially, this method was analyzed for discrete states and actions. Functions were then approximated by lookup tables. Most of the real problems have large input spaces and/or continuous actions. So, other function approximators have to ...


Nonlinear mapping with minimal supervised learning

Tolat, V.V.; Peterson, A.M. System Sciences, 1990., Proceedings of the Twenty-Third Annual Hawaii International Conference on, 1990

The problem of interpolating unknown mappings from known mappings is addressed. This problem arises when a large number of mappings must be learned and it is impractical to train the network on all possible mappings. Described is a network model that can learn nonlinear mappings with a minimal amount of supervised training. A combination of supervised and supervised learning is ...


Realtime Audio to Score Alignment for Polyphonic Music Instruments, using Sparse Non-Negative Constraints and Hierarchical HMMS

Cont, A. Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, 2006

We present a new method for realtime alignment of audio to score for polyphonic music signals. In this paper, we will be focusing mostly on the multiple-pitch observation algorithm proposed based on realtime non-negative matrix factorization with sparseness constraints and hierarchical hidden Markov models for sequential modeling using particle filtering for decoding. The proposed algorithm has the advantage of having ...


Learning with a probabilistic teacher

Agrawala, A. Information Theory, IEEE Transactions on, 1970

The Bayesian learning scheme is computationally infeasible for most of the unsupervised learning problems. This paper suggests a learning scheme, "learning with a probabilistic teacher," which works with unclassified samples and is computationally feasible for many practical problems. In this scheme a sample is probabilistically assigned with a class with appropriate probabilities computed using all the information available: Then the ...


Boundary region sensitive classification for the counter-propagation neural network

Kovacs, L.; Terstyanszky, G. Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, 2000

The basic problem of classification priori unknown faults is related to re- arrangement of existing classes and/or introduction of new classes that requires management of uncertain regions where input pattern vectors may belong to several classes. The counter-propagation neural network (CPN) was selected to investigate the classification problems because it integrates both supervised and unsupervised learning to support diagnosis of ...


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