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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Digital Hardware Architectures of Kohonen's Self Organizing Feature Maps with Exponential Neighboring Function

Pena, J.; Vanegas, M.; Valencia, A. Reconfigurable Computing and FPGA's, 2006. ReConFig 2006. IEEE International Conference on, 2006

Kohonen maps are self-organizing neural networks that categorize input data, capturing its topology and probability distribution. Efficient hardware implementations of such maps require the definition of a certain number of simplifications to the original algorithm. In particular, multiplications have to be avoided by means of choices in the distance metric, the neighborhood function and the set of learning parameter values. ...


Multinomial PCA for extracting major latent topics from document streams

Kimura, M.; Saito, K.; Ueda, N. Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on, 2005

We propose a new unsupervised learning method called multinomial PCA (MuPCA) for efficiently extracting the major latent topics from a document stream based on the "bag-of-words" (BOW) representation of a document. Unlike PCA, MuPCA follows a suitable probabilistic generative model for the document stream represented as time-series of word-frequency vectors. Using real data of document streams on the Web, we ...


Intelligent information acquisition design beyond last mile challenge

Szu, H. Information Acquisition, 2005 IEEE International Conference on, 2005

Current designs of info tech (IT) are relying on a traditional backbone. The most serious bottleneck, however in users' minds, is not the technology; rather the quality of service (QoS) for IP is not intelligent. Although our software-enhanced design is likewise constrained in hardware, but a recent breakthrough in the brain-style information science (BIS) based on the physics of unsupervised ...


Fuzzy min-max neural networks - Part 2: Clustering

Simpson, P.K. Fuzzy Systems, IEEE Transactions on, 1993

First Page of the Article ![](/xploreAssets/images/absImages/00390282.png)


Space Efficient String Mining under Frequency Constraints

Fischer, J.; Makinen, V.; Valimaki, N. Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on, 2008

Let D1 and D2 be two databases (i.e. multisets) of d strings, over an alphabet Sigma, with overall length n. We study the problem of mining discriminative patterns between D1 and D2 - e.g., patterns that are frequent in one database but not in the other, emerging patterns, or patterns satisfying other frequency-related constraints. Using the algorithmic framework by Hui ...


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Standards related to Unsupervised learning

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No standards are currently tagged "Unsupervised learning"


Jobs related to Unsupervised learning

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