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 ...



Most published Xplore authors for Unsupervised learning

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Xplore Articles related to Unsupervised learning

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Improved random walker algorithm for image segmentation

Yusuf Artan; Imam Samil Yetik Image Analysis & Interpretation (SSIAI), 2010 IEEE Southwest Symposium on, 2010

General purpose image segmentation is one of the important and challenging problems in image processing. Objective of image segmentation is to group regions with coherent cues such as intensity, texture, color and shape together. Most of the earlier studies on this issue are based on supervised and unsupervised learning methods. In this paper, we develop a semi-supervised image segmentation technique ...


Self-organization neural network for multiple texture image segmentation

Woobeom Lee; Wookhyun Kim TENCON 99. Proceedings of the IEEE Region 10 Conference, 1999

Texture analysis is an important technique in many image processing areas, such as scene segmentation, object recognition, and shape and depth perception. But no efficient methods captures all aspects of the very diverse texture family including natural scenes. We propose a novel approach for efficient texture image analysis that use unsupervised learning schemes for the texture recognition task. The self-organization ...


Neural networks

J. Staley Neural Networks, 1999. IJCNN '99. International Joint Conference on, 1999

A history of the development of neural nets is given. Types of learning are discussed. Applications are listed, with brief comments on nets' suitability to a few of them


Notice of Retraction<BR>Application of weighted distance in clustering of hydraulic press

Wang Ya; Li Da-hua; Zhang Si-bing Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, 2010

Notice of Retraction After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper. The presenting author of this paper ...


Myoelectric signal classification using evolutionary hybrid RBF-MLP networks

A. M. S. Zalzala; N. Chaiyaratana Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, 2000

This paper introduces a hybrid neural structure using radial-basis functions (RBF) and multilayer perceptron (MLP) networks. The hybrid network is composed of one RBF network and a number of MLPs, and is trained using a combined genetic/unsupervised/supervised learning algorithm. The genetic and unsupervised learning algorithms are used to locate the centres of the RBF part in the hybrid network. In ...


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Educational Resources on Unsupervised learning

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eLearning

Improved random walker algorithm for image segmentation

Yusuf Artan; Imam Samil Yetik Image Analysis & Interpretation (SSIAI), 2010 IEEE Southwest Symposium on, 2010

General purpose image segmentation is one of the important and challenging problems in image processing. Objective of image segmentation is to group regions with coherent cues such as intensity, texture, color and shape together. Most of the earlier studies on this issue are based on supervised and unsupervised learning methods. In this paper, we develop a semi-supervised image segmentation technique ...


Self-organization neural network for multiple texture image segmentation

Woobeom Lee; Wookhyun Kim TENCON 99. Proceedings of the IEEE Region 10 Conference, 1999

Texture analysis is an important technique in many image processing areas, such as scene segmentation, object recognition, and shape and depth perception. But no efficient methods captures all aspects of the very diverse texture family including natural scenes. We propose a novel approach for efficient texture image analysis that use unsupervised learning schemes for the texture recognition task. The self-organization ...


Neural networks

J. Staley Neural Networks, 1999. IJCNN '99. International Joint Conference on, 1999

A history of the development of neural nets is given. Types of learning are discussed. Applications are listed, with brief comments on nets' suitability to a few of them


Notice of Retraction<BR>Application of weighted distance in clustering of hydraulic press

Wang Ya; Li Da-hua; Zhang Si-bing Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, 2010

Notice of Retraction After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper. The presenting author of this paper ...


Myoelectric signal classification using evolutionary hybrid RBF-MLP networks

A. M. S. Zalzala; N. Chaiyaratana Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, 2000

This paper introduces a hybrid neural structure using radial-basis functions (RBF) and multilayer perceptron (MLP) networks. The hybrid network is composed of one RBF network and a number of MLPs, and is trained using a combined genetic/unsupervised/supervised learning algorithm. The genetic and unsupervised learning algorithms are used to locate the centres of the RBF part in the hybrid network. In ...


More eLearning Resources

IEEE.tv Videos

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IEEE-USA E-Books

  • Temporal Pattern Learning in a Spiking Neuron Chain

    A simple neural network demonstrates lhe ability to learn Morse axle-like temporal patterns, presented via a microphone. A neuronal model, which provides facility for 'spiking' neurons, is used as the basis for this network. The network consists mainly of a chain of neurons, connected so as to lire in sequence. After a period of unsupervised learning, the chain is capable of repeating a temporal pattern upon which it has been trained. The system is particularly robust with respect to noise in the input pattern. Suggestions are made as to how this system could be extended, by replacing the simple chain structure with complex trees and interconnected 'blobs' of neurons. The example of birdsong as a biological system of temporal pattern learning is used as inspiration for further work in this area.

  • Change of Representation

    In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi- Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low- density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tÿbingen. Schölkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large- Margin Classifiers (2000), and Kernel Methods in Computational B iology (2004), all published by The MIT Press.</P

  • Self-Organization in Image Retrieval

    This chapter provides a comprehensive study on modern approaches in the area of image indexing and retrieval on the use of Self-Organization as a core enabling technology. It begins with the development of Content-based image retrieval (CBIR) systems, which includes the implementation of a radial basis function (RBF) based relevance feedback (RF) method. The chapter presents automatic and semiautomatic methods in multimedia retrieval, using the pseudo- RF for minimizing user interaction in a retrieval process. It introduces a framework for a novel extension of the self-organizing tree map (SOTM) for hierarchical clustering, the Directed SOTM (DSOTM). It demonstrates an optimized architecture for an automatic retrieval system based on collaboration between the DSOTM and the Genetic Algorithm (GA). A study on the feasibility of the proposed feature weight detection scheme in conjunction with the DSOTM, SOTM, and self-organizing feature map (SOFM) classifier techniques is presented.

  • The Self-Organizing Hierarchical Variance Map

    This chapter introduces and develops a new model for clustering data, offering a number of enhancements and features over the self-organizing tree map (SOTM). The model is known as the Self-Organizing Hierarchical Variance Map (SOHVM). The chapter highlights some of its limitations. In so doing, a motivation is provided for a more advanced clustering algorithm, one that retains some of the desirable properties of the SOTM. The component responsible for mapping local variance information is known as a Hebbian Maximal Eigenfilter (HME). It outlines and justifies the key components and principles of operation for the new model. In addition, the implementation details are discussed. Finally, a series of visual simulations on synthetic two-dimensional (2D) data are presented, with the goal of providing a simple and clear demonstration of the new model in operation, highlighting some of its key features and strengths over popular existing architectures from the literature.

  • Unsupervised Learning

    In this chapter, a general review of Unsupervised Learning is conducted. Generic clustering issues are first defined and explained. A survey of traditional approaches to Unsupervised Learning is then presented, and the chapter concludes in with a discussion of assessment measures and limitations in the evaluation of clustering solutions. It presents a brief survey of the issues that need to be considered in assessing the validity of unsupervised clustering results. Distance metrics, cluster quality, and cluster validity are each vast topics unto themselves and become essential, yet difficult considerations in the evaluation of a clustering solution within unsupervised contexts. These are expanded upon in the chapter. It outlines some of the more popular clustering approaches from the literature namely, Iterative Mean- Squared Error Approaches, Mixture Decomposition Approaches, Agglomerative Hierarchical Approaches, Graph-Theoretic Approaches, Evolutionary Approaches and Neural Network Approaches.

  • Particle Filtering for Nonparametric Bayesian Matrix Factorization

    Many unsupervised learning problems can be expressed as a form of matrix factorization, reconstructing an observed data matrix as the product of two matrices of latent variables. A standard challenge in solving these problems is determining the dimensionality of the latent matrices. Nonparametric Bayesian matrix factorization is one way of dealing with this challenge, yielding a posterior distribution over possible factorizations of unbounded dimensionality. A drawback to this approach is that posterior estimation is typically done using Gibbs sampling, which can be slow for large problems and when conjugate priors cannot be used. As an alternative, we present a particle filter for posterior estimation in nonparametric Bayesian matrix factorization models. We illustrate this approach with two matrix factorization models and show favorable performance relative to Gibbs sampling.

  • Microbiological Image Analysis Using Self-Organization

    This chapter considers the potential and flexibility of self-organizing tree map (SOTM) based and self-organizing hierarchical variance map (SOHVM) based learning for tasks in microbiological image analysis. As a demonstration of the SOHVM's ability to mine topological information from an input space, the chapter describes with an example for how such information can be used to simplify the task of visualizing a large three-dimensional (3D) stack of phase-contrast acquired plant chromosomes imaged during an advanced state of mitosis (cell division). The chapter considers two types of microbiological image data in order to demonstrate the potential for the proposed algorithm to achieve unsupervised, fully automatic segmentations. It shows examples of utilizing this automated property of the SOHVM to seek more natural segmentations of gray-level and higher order, multidimensional feature descriptions, with examples for the clustering of texture information and Local gray-level-based statistics.

  • Self-Organization

    This chapter presents the principles of Self-Organization, and focuses on Adaptive Resonance Theory (ART) and Self-Organizing Map (SOM) neural networks. It investigates the theoretic basis of formulations of these neural networks, and illustrates a few examples. The structures of these networks and their learning algorithms are also thoroughly explored in the chapter. The ART architecture is a specifically designed neural network to overcome the stability-plasticity dilemma. It is described using nonlinear differential equations. In addition to ART and SOM, there are two other fixed approaches that could be considered fundamental, namely, Neural Gas and the Hierarchical Feature Map. While both are strongly related to the SOM in terms of the learning mechanism, they each have spawned a range of newer architectures are introduced in the chapter. The chapter explains other popular architectures to have emerged based on similar principles of Self-Organization, drawing a distinction between static and dynamic architectures.

  • Closing Remarks and Future Directions

    The focal points of the book lay in the design and development of two novel models for unsupervised learning or data clustering, based on dynamic Self- Organization: namely, the self-organizing tree map (SOTM) and The Self- Organizing Hierarchical Variance Map (SOHVM). This chapter summarizes the main properties and recommendations in the use of such models, and discusses some potential directions for future research and application. The real advantage of creating a self-organized clustering as opposed to most other clustering methods, lies in the availability of the resulting topological map. Mining the topology, as opposed to assuming one through imposing predetermined lattice, can be leveraged for very specific tasks. The chapter focuses on three major categories of task: namely, dynamic navigation through information repositories; knowledge-assisted visualization; and path-based trajectory analysis. In each category, there is a common Theme-where there is topology, there is context, and context can assist in conveying or extracting knowledge.

  • Contributors

    In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi- Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low- density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tÿbingen. Schölkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large- Margin Classifiers (2000), and Kernel Methods in Computational B iology (2004), all published by The MIT Press.</P



Standards related to Unsupervised learning

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Jobs related to Unsupervised learning

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