Supervised learning

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Supervised learning is the machine learning task of inferring a function from supervised training data. (Wikipedia.org)






Conferences related to Supervised learning

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ICC 2021 - IEEE International Conference on Communications

IEEE ICC is one of the two flagship IEEE conferences in the field of communications; Montreal is to host this conference in 2021. Each annual IEEE ICC conference typically attracts approximately 1,500-2,000 attendees, and will present over 1,000 research works over its duration. As well as being an opportunity to share pioneering research ideas and developments, the conference is also an excellent networking and publicity event, giving the opportunity for businesses and clients to link together, and presenting the scope for companies to publicize themselves and their products among the leaders of communications industries from all over the world.


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


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Periodicals related to Supervised learning

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


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Most published Xplore authors for Supervised learning

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Xplore Articles related to Supervised learning

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Machine Learning Applied to Cognitive Communications

Cognitive Communications: Distributed Artificial Intelligence (DAI), Regulatory Policy and Economics, Implementation, None

This chapter contains sections titled:IntroductionState of the ArtLearning TechniquesAdvantages and Disadvantages of Applying Machine Learning to Cognitive Radio NetworksConclusionsAcknowledgementReferences


Formation and variability of orientation preference maps in visual cortex: an approach based on normalized Gaussian arrays

2005 9th International Workshop on Cellular Neural Networks and Their Applications, 2005

This work explores formation and variability of orientation preference maps in visual cortex based on normalized Gaussian arrays. An orientation preference map, which has been measured to sketch the orientation preference of neighboring neurons in visual cortex, is emulated by a network of weighted normalized Gaussian arrays. Here the orientation preference map is represented by a set of paired data, ...


Neurocomputing motivation, models, and hybridization

Computer, 1996

None


Web Image Clustering Based on Multi-instance

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008

In image retrieval and annotation, multi-instance learning has been studied actively. Most of the methods solve the MIL problem in a supervised way. In this paper, we proposed two unsupervised frameworks for clustering multi- instance objects based on expectation maximization (EM) and iterative heuristic optimization respectively. For each framework, we introduced three new algorithms of finding users' interests on specific ...


A neural demodulator for amplitude shift keying signals

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994

A neural demodulator is proposed for amplitude shift keying (ASK) signal. It has several important features compared with conventional linear methods. First, necessary functions for ASK demodulation, including wide-band noise rejection, pulse waveform shaping, and decoding, can be embodied in a single neural network. This means these functions are not separately designed but unified in a learning and organizing process. ...


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Educational Resources on Supervised learning

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IEEE-USA E-Books

  • Machine Learning Applied to Cognitive Communications

    This chapter contains sections titled:IntroductionState of the ArtLearning TechniquesAdvantages and Disadvantages of Applying Machine Learning to Cognitive Radio NetworksConclusionsAcknowledgementReferences

  • Formation and variability of orientation preference maps in visual cortex: an approach based on normalized Gaussian arrays

    This work explores formation and variability of orientation preference maps in visual cortex based on normalized Gaussian arrays. An orientation preference map, which has been measured to sketch the orientation preference of neighboring neurons in visual cortex, is emulated by a network of weighted normalized Gaussian arrays. Here the orientation preference map is represented by a set of paired data, each comprising a relative neuron location and the relevant orientation preference, and the mapping structure from the location on a two-dimensional lattice to the orientation preference is then realized by learning a network of normalized Gaussian arrays subject to the paired data. As a result, varieties of mapping structures of different species are essentially characterized by the array number in a network. For example, the icecube structure can be well emulated by using one Gaussian array, the pinwheels structure, that looks like a superposition of two cross icecubes, can be characterized by a network of two Gaussian arrays, and the salt&pepper structure is simply a result of a null Gaussian array, of which the width of each Gaussian unit is set sufficiently large.

  • Neurocomputing motivation, models, and hybridization

    None

  • Web Image Clustering Based on Multi-instance

    In image retrieval and annotation, multi-instance learning has been studied actively. Most of the methods solve the MIL problem in a supervised way. In this paper, we proposed two unsupervised frameworks for clustering multi- instance objects based on expectation maximization (EM) and iterative heuristic optimization respectively. For each framework, we introduced three new algorithms of finding users' interests on specific Web images without any manual labeled data. And comparative studies have shown the effectiveness of the proposed algorithms.

  • A neural demodulator for amplitude shift keying signals

    A neural demodulator is proposed for amplitude shift keying (ASK) signal. It has several important features compared with conventional linear methods. First, necessary functions for ASK demodulation, including wide-band noise rejection, pulse waveform shaping, and decoding, can be embodied in a single neural network. This means these functions are not separately designed but unified in a learning and organizing process. Second, these functions can be self-organized through the learning. Supervised learning algorithms, such as the backpropagation algorithm, can be applied for this purpose. Finally, both wide-band noise rejection and a very sharp waveform response can be simultaneously achieved. It is very difficult to be done by linear filtering. Computer simulation demonstrates efficiency of the proposed method.<<ETX>>

  • Statistical learning: data mining and prediction with applications to medicine and genomics

    Summary form only given. This tutorial is devoted to an important segment of statistical learning techniques related to the problem of supervised learning, which aims at predicting the value of an outcome given a number of inputs. Theoretical material is oriented mainly towards methods and concepts. The introduction outlines general aspects of statistical learning, together with motivations for its applications in medicine and genomics. The second part deals with the main theoretical aspects of supervised learning, including a short overview of statistical decision theory, with the emphasis on the problem of trade-off between bias and variance. Attention is further paid to linear methods, applied to both regression and classification problems. In the presentation of neural networks applied to statistical learning, stress is placed on multi-layer perceptrons and training algorithms based on gradient search techniques. Various issues important in practice are given considerable attention, including cross-validation techniques and the choice of suitable learning procedures.

  • Estimation of the Bayesian network architecture for object tracking in video sequences

    It was recently proposed the use of Bayesian networks for object tracking. Bayesian networks allow modeling the interaction among detected trajectories, in order to obtain reliable object identification in the presence of occlusions. However, the architecture of the Bayesian network has been defined using simple heuristic rules, which fail in many cases. This paper addresses the above problem and presents a new method to estimate the network architecture from the video sequences using supervised learning techniques. Experimental results are presented showing that significant performance gains (increase of accuracy and decrease of complexity) are achieved by the proposed methods.

  • Using logistic regression to initialise reinforcement-learning-based dialogue systems

    We investigate the use of logistic regression (LR) to initialise reinforcement learning (RL)-based dialogue systems with models of human dialogue strategies. LR produces accurate predictions and performs feature selection. We illustrate this technique in exploring human multimodal clarification strategies, observed in a Wizard-of-Oz experiment. We use it to initialise an RL-based system with features which significantly influence human behaviour. We show that the strategy applied by the human wizards is sensitive to different dialogue contexts. Furthermore we show that for predicting clarification behaviour the logistic models improve over the baseline on average twice as much as the supervised learning techniques used in previous work.

  • A Semi-Supervised Relief Based Feature Extraction Algorithm

    Local Feature Extraction (LFE) algorithm is an effective feature extraction method developed in recent years. One of the shortcomings of the current LFE algorithm is that it can only process labeled data, and does not work well when the amount of the labeled data is limited. However, it is usually easy to obtain large amount of unlabeled data but only a few labeled data. In this paper, we propose a new feature extraction algorithm, called Semi-Supervised LFE (SSLFE), which can handle both labeled and unlabeled data to perform feature extraction. In the proposed algorithm, the labeled data are used to maximize the margin and the unlabeled data are used as regulations with respect to the intrinsic geometric structure of the data. The final projection matrix can be obtained by eigenvalue decomposition. Experiments on several datasets demonstrate that SSLFE achieves much higher classification accuracy than LFE.

  • Classifier based duplicate record elimination for query results from web databases

    Record matching is an essential step in duplicate detection as it identifies records representing same real-world entity. Supervised record matching methods require users to provide training data and therefore cannot be applied for web databases where query results are generated on-the-fly. To overcome the problem, a new record matching method named Unsupervised Duplicate Elimination (UDE) is proposed for identifying and eliminating duplicates among records in dynamic query results. The idea of this paper is to adjust the weights of record fields in calculating similarities among records. Three classifiers namely weight component similarity summing classifier, support vector machine classifier and one class support vector machine classifier are iteratively employed with UDE where the first classifier utilizes the weights set to match records from different data sources. With the matched records as positive dataset and non duplicate records as negative set, the second classifier identifies new duplicates. Then, one-class support vector machine classifier is employed for further detecting the duplicates. The iteration stops when no duplicates can be identified. Thus, this paper takes advantage of dissimilarity among records from web databases and solves the online duplicate detection problem.



Standards related to Supervised learning

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