35 resources related to Unsupervised learning
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Authors are invited to submit previously unpublished papers describing theoretical advances, applications, and ideas in the fields of Information Sciences and Systems including signal and image processing and analysis; communications and information theory; systems biology and biological control; computer engineering; systems and control theory; and photonic systems.
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
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 humanitaria
Bui, T.D.; Duy Khuong Nguyen; Tien Dat Ngo Intelligent Information and Database Systems, 2009. ACIIDS 2009. First Asian Conference on, 2009
Machine learning is the field that is dedicated to the design and development of algorithms and techniques that allow computers to "learn". Two common types of learning that are often mentioned are supervised learning and unsupervised learning. One often understands that in supervised learning, the system is given the desired output, and it is required to produce the correct output ...
Li Deng; Xiao Li Audio, Speech, and Language Processing, IEEE Transactions on, 2013
Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including the ubiquitously used hidden Markov model, discriminative learning, structured sequence learning, Bayesian learning, and adaptive learning. Moreover, ML can and occasionally does use ASR as a large-scale, realistic application to rigorously test the effectiveness of a given technique, and to inspire new problems ...
Daxin Tian; Yanheng Liu; Da Wei Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, 2006
A dynamic growing neural network (DGNN) for supervised learning of pattern recognition or unsupervised learning of clustering is presented. The main ideas included in DGNN are growing, resonance, and post-prune. DGNN is called dynamic growing because it is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if ...
Shouyi Wang; Chaovalitwongse, W.; Babuska, R. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2012
Over the past decades, machine learning techniques, such as supervised learning, reinforcement learning, and unsupervised learning, have been increasingly used in the control engineering community. Various learning algorithms have been developed to achieve autonomous operation and intelligent decision making for many complex and challenging control problems. One of such problems is bipedal walking robot control. Although still in their early ...
Krishnamurthy, S.; Thamilarasu, G.; Bauckhage, C. Computational Science and Engineering, 2009. CSE '09. International Conference on, 2009
As the capabilities of sensor networks evolve, we need to address the challenges that will help in shifting the perception of sensor networks as being merely a data-gathering network to that of a network that is capable of learning and making decisions autonomously. This shift in intelligence from the edge to the nodes in the network is particularly relevant in ...
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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 ...