1,753 resources 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 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
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.
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 ...
Ranasinghe, N.; Wei-Min Shen Learning and Adaptive Behaviors for Robotic Systems, 2008. LAB-RS '08. ECSIS Symposium on, 2008
This paper presents a learning algorithm called surprise-based learning (SBL) capable of providing a physical robot the ability to autonomously learn and plan in an unknown environment without any prior knowledge of its actions or their impact on the environment. This is achieved by creating a model of the environment using prediction rules. A prediction rule describes the observations of ...
Jain, V.; Saul, L.K. Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on, 2004
Many problems in voice recognition and audio processing involve feature extraction from raw waveforms. The goal of feature extraction is to reduce the dimensionality of the audio signal while preserving the informative signatures that, for example, distinguish different phonemes in speech or identify particular instruments in music. If the acoustic variability of a data set is described by a small ...
Simpson, P.K. Fuzzy Systems, IEEE Transactions on, 1993
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Marvel, J.A.; Newman, W.S. Automation Science and Engineering, IEEE Transactions on, 2011
We present here a framework for the generation, application, and assessment of assistive models for the purpose of aiding automated robotic parameter optimization methods. Our approach represents an expansion of traditional machine learning implementations by employing models to predict the performances of input parameter sequences and then filter a potential population of inputs prior to evaluation on a physical system. ...
Gashler, M.; Ventura, D.; Martinez, T. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 2011
We present an algorithm for manifold learning called manifold sculpting , which utilizes graduated optimization to seek an accurate manifold embedding. An empirical analysis across a wide range of manifold problems indicates that manifold sculpting yields more accurate results than a number of existing algorithms, including Isomap, locally linear embedding (LLE), Hessian LLE (HLLE), and landmark maximum variance unfolding (L-MVU), ...
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