IEEE Organizations related to Nearest Neighbor Methods

Back to Top

No organizations are currently tagged "Nearest Neighbor Methods"



Conferences related to Nearest Neighbor Methods

Back to Top

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

All fields of satellite, airborne and ground remote sensing.


2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Artificial Intelligence, Control and Systems, Cyber-physical Systems, Energy and Environment, Industrial Informatics and Computational Intelligence, Robotics, Network and Communication Technologies, Power Electronics, Signal and Information Processing


2019 IEEE 10th GCC Conference & Exhibition (GCC)

To Enhance the new Technologies to the Young Professionals and the Experts in difference areas of Engineering Fields in GCC

  • 2017 9th IEEE-GCC Conference and Exhibition (GCCCE)

    The conference offers an excellent opportunity for scientists, innovators, and engineers to interact, share experiences and present their latest research with peers in a multidisciplinary engineering background. The conference provides a forum for discussion among professionals from industries, academia and research institutions. This conference also includes tutorials and workshops as well as an industrial track and industrial exhibition.

  • 2015 IEEE 8th GCC Conference and Exhibition (GCCCE)

    The conference provides a forum for discussions among professionals from industries, academia and research institutions. The conference offers an excellent opportunity for scientists, innovators and engineers to interact, share experiences and present their latest research with peers in a multidisciplinary engineering background. This conference also includes tutorials and workshops as well as industrial exhibitions. The tutorial sessions will be held on the first day of the conference. Special industrial track is also included.

  • 2013 7th IEEE GCC Conference and Exhibition (GCC)

    The 7th IEEE GCC conference and exhibition will be held from 17th to 20th of November 2013 at Doha, Qatar, under the patronage of H.E. the Prime Minister and Foreign Minister Sheikh Hamad Bin Jassim Al-Thani of the State of Qatar. The conference provides a forum for discussion between professionals from industry, academia and research institutions. The conference presents an excellent opportunity for scientists, innovators and engineers to interact and share their experiences with other peers of multidisciplinary engineering background. This conference will run for four days and will be accompanied by several tutorials and workshops as well as an industrial exhibition. Tutorial sessions will be held on the first day of the conference.

  • 2011 IEEE GCC Conference and Exhibition (GCC)

    The conference provides a forum for professional engineers, scientists and academics engaged in research and development to convene and present their latest scholarly work and applications. The conference will be accompanied by an industrial exhibition and diverse tutorial sessions. Papers are invited in all areas of electrical and electronic engineering.

  • 2009 5th IEEE GCC Conference & Exhibition, "Innovative Engineering for Sustainable Environment"

    The conference will provide a forum for professionalengineers, scientists and academics engaged in research and development to convene and present their latest scholarly work and applications in industry. It will also provide engineers with an opportunity to interact and share their knowledge and experiences in technology applications. The conference will be accompanied by a comprehensive industrial exhibition. Diverse tutorial sessions will be held on the first day of the conference.

  • 2006 IEEE GCC Conference


2018 11th International Conference on Human System Interaction (HSI)

The HSI conference will cover research topics related to the traditional combination of hardware, software and human factors as well as theories and methods of psychology, and communication. The conference will focus not only on theories but also on practical insights related to Human System Interaction. HSI is widely applicable to all types of human activity including manufacturing, transport, supply chain, medical treatment , personal care (aged care, the elderly , the disabled), tele-health, education, business, government, the household and remote monitoring and control. Additionally, there are many new research areas open to Human System Interaction. During the conference we would like to inspire and provoke the audience to work on new ideas and solutions that could become standards for future Human System Interaction applications.

  • 2017 10th International Conference on Human System Interactions (HSI)

    The HSI2017 conference covers both theory and applications in all the area of human system interaction. The topics of interests include, but are not limited to:- Artificial Intelligence- Human Machine Interaction- Education and Training- Cyber Security in HSI- Sociological ad Psychological Aspects of HSI- Cyber Physical Systems- Human Space Computing- Internet of Things and Smart Homes- Health Care and Assistive Devices- Extreme Interfaces- Humanity in Wireless Mesh Network- Vehicular System- Multimedia Human Communication- Personal Communication System- Personal Mobile Ad-hoc System- Wearable Network and System- Network Control and Management- Security, Privacy and Trust- Human Dependable System- Multi-agent System and Applications

  • 2016 9th International Conference on Human System Interactions (HSI)

    Rapid improvement of information and computation technologies leads to the extending and boosting of human system interaction. A large variety of human activities are supported with the development of HSI in aspects of manufacturing, education, business, health and management. International Conference series on Human System Interactions have been serving as a platform for exchanging ideas, knowledge, skills and experiences in interactions between human and systems.

  • 2015 8th International Conference on Human System Interactions (HSI)

    The HSI conference will cover research topics related to the traditional combination of hardware, software and human factors as well as theories and methods of psychology, and communication. The conference will focus not only on theories but also on practical insights related to Human System Interaction. HSI is widely applicable to all types of human activity including manufacturing, transport, supply chain, medical treatment , personal care (aged care, the elderly , the disabled), tele-health, education, business, government, the household and remote monitoring and control. Additionally, there are many new research areas open to Human System Interaction. During the conference we would like to inspire and provoke the audience to work on new ideas and solutions that could become standards for future Human System Interaction applications.

  • 2014 7th International Conference on Human System Interactions (HSI)

    Rapid changes of information technologies lead to boosting the strength and efficiency of human activity. These technologies have an influence on every particular area of human life, including education, science, business, leisure time, entertainment, state administration, health care. Human life is greatly dependent on these systems

  • 2013 6th International Conference on Human System Interactions (HSI)

    Rapid changes of information technologies lead to boosting the strength and efficiency of human activity. These technologies have an influence on every particular area of human life, including education, science, business, leisure time, entertainment, state administration, health care. Human life is greatly dependent on these systems

  • 2012 5th International Conference on Human System Interactions (HSI)

    The conference covers theory, design and application of human-system interactions in the areas of science, education, business, industry, services, humanity, environment, health, and government.

  • 2011 4th International Conference on Human System Interactions (HSI)

    The recent development of computational technologies contributes to introducing intelligent and interactive systems for supporting and extending human activities. International Conference series on Human System Interactions have been providing a platform for interdisciplinarily exchanging ideas, knowledge, skills and experiences in interactions between human and systems.

  • 2010 3rd International Conference on Human System Interactions (HSI)

    Rapid changes of information technologies lead to boosting the strength and efficiency of human activity. These technologies have an influence on every particular area of human life, including education, science, business, leisure time, entertainment, state administration, health care. Human life is greatly dependent on these systems efficiency and performance. HSI conference has been prepared as a platform for exchanging ideas, knowledge, skills and experiences in interactions between man and these s

  • 2009 2nd Conference on Human System Interactions (HSI)

    Computers are today integrant parts of our life, embedded in more and more devices, objects, and systems. So the interactions between Human and Computers has evolved toward interactions between Human and Systems including the larger range of ubiquitous, pervasive computing. Human Systems Interaction 2009 aims to represent a meeting point for different communities, where to share experience and knowledge related to the interaction of human beings with such a broader range of systems.

  • 2008 Conference on Human System Interactions (HSI)


More Conferences

Periodicals related to Nearest Neighbor Methods

Back to Top

No periodicals are currently tagged "Nearest Neighbor Methods"


Most published Xplore authors for Nearest Neighbor Methods

Back to Top

Xplore Articles related to Nearest Neighbor Methods

Back to Top

Correction to “K Nearest Neighbour Joins for Big Data on MapReduce: A Theoretical and Experimental Analysis”

IEEE Transactions on Knowledge and Data Engineering, 2018

Presents corrections to the paper, “K nearest neighbour joins for big data on MapReduce: A theoretical and experimental analysis,” (Song, G., et al), IEEE Trans. Knowl. Data Eng., vol. 28, no. 9, pp. 2376–2392, Sep. 2016.


Metric Driven Classification: A Non-Parametric Approach Based on the Henze–Penrose Test Statistic

IEEE Transactions on Image Processing, 2018

Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the usage of a non-parametric distribution-free metric, known as the Henze-Penrose ...


Distribution Sensitive Product Quantization

IEEE Transactions on Circuits and Systems for Video Technology, 2018

Product quantization (PQ) seems to have become the most efficient framework of performing approximate nearest neighbor (ANN) search for high-dimensional data. However, almost all existing PQ-based ANN techniques uniformly allocate precious bit budget to each subspace. This is not optimal, because data are often not evenly distributed among different subspaces. A better strategy is to achieve an improved balance between ...


Deep Discrete Supervised Hashing

IEEE Transactions on Image Processing, 2018

Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and feature learning based deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information ...


Feature Selection for Optimized High-Dimensional Biomedical Data Using an Improved Shuffled Frog Leaping Algorithm

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018

High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. The purpose of feature selection is ...


More Xplore Articles

Educational Resources on Nearest Neighbor Methods

Back to Top

IEEE-USA E-Books

  • Correction to “K Nearest Neighbour Joins for Big Data on MapReduce: A Theoretical and Experimental Analysis”

    Presents corrections to the paper, “K nearest neighbour joins for big data on MapReduce: A theoretical and experimental analysis,” (Song, G., et al), IEEE Trans. Knowl. Data Eng., vol. 28, no. 9, pp. 2376–2392, Sep. 2016.

  • Metric Driven Classification: A Non-Parametric Approach Based on the Henze–Penrose Test Statistic

    Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the usage of a non-parametric distribution-free metric, known as the Henze-Penrose test statistic to obtain bounds for the k-nearest neighbors (k-NN) classification accuracy. Simulation results demonstrate the effectiveness and the reliability of this metric in estimating the inter-class separability. In addition, the proposed bounds on the k-NN classification are exploited for evaluating the efficacy of different pre-processing techniques as well as selecting the least number of features that would achieve the desired classification performance.

  • Distribution Sensitive Product Quantization

    Product quantization (PQ) seems to have become the most efficient framework of performing approximate nearest neighbor (ANN) search for high-dimensional data. However, almost all existing PQ-based ANN techniques uniformly allocate precious bit budget to each subspace. This is not optimal, because data are often not evenly distributed among different subspaces. A better strategy is to achieve an improved balance between data distribution and bit budget within each subspace. Motivated by this observation, we propose to develop an optimized PQ (OPQ) technique, named distribution sensitive PQ (DSPQ) in this paper. The DSPQ dynamically analyzes and compares the data distribution based on a newly defined aggregate degree for high-dimensional data; whenever further optimization is feasible, resources such as memory and bits can be dynamically rearranged from one subspace to another. Our experimental results have shown that the strategy of bit rearrangement based on aggregate degree achieves modest improvements on most datasets. Moreover, our approach is orthogonal to the existing optimization strategy for PQ; therefore, it has been found that distribution sensitive OPQ can even outperform previous OPQ in the literature.

  • Deep Discrete Supervised Hashing

    Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and feature learning based deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy. On the other hand, feature learning based deep hashing, which integrates deep feature learning and hash- code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning. The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing. The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure. However, most deep supervised hashing methods cannot use the supervised information to directly guide both discrete (binary) coding procedure and deep feature learning procedure in the same framework. In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH). DDSH is the first deep hashing method which can utilize pairwise supervised information to directly guide both discrete coding procedure and deep feature learning procedure and thus enhance the feedback between these two important procedures. Experiments on four real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.

  • Feature Selection for Optimized High-Dimensional Biomedical Data Using an Improved Shuffled Frog Leaping Algorithm

    High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. The purpose of feature selection is to not only identify a feature subset from an original set of features [without reducing the predictive accuracy of classification algorithm] but also reduce the computation overhead in data mining. In this paper, we present our improved shuffled frog leaping algorithm which introduces a chaos memory weight factor, an absolute balance group strategy, and an adaptive transfer factor. Our proposed approach explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data. To evaluate the effectiveness of our proposed method, we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. Experimental results show that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.

  • Wide-Area Monitoring of Power Systems Using Principal Component Analysis and $k$-Nearest Neighbor Analysis

    Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate analysis known as Principal Component Analysis (PCA) and time series analysis known as k-Nearest Neighbor (kNN) analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.

  • A Clinical Decision Support Framework for Heterogeneous Data Sources

    To keep pace with the developments in medical informatics, health medical data is being collected continually. But, owing to the diversity of its categories and sources, medical data has become so complicated in many hospitals that it now needs a clinical decision support (CDS) system for its management. To effectively utilize the accumulating health data, we propose a CDS framework that can integrate heterogeneous health data from different sources such as laboratory test results, basic information of patients, and health records into a consolidated representation of features of all patients. Using the electronic health medical data so created, multilabel classification was employed to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients' health issues more efficiently. Once the physician diagnoses the disease of a patient, the next step is to consider the likely complications of that disease, which can lead to more diseases. Previous studies reveal that correlations do exist among some diseases. Considering these correlations, a k-nearest neighbors algorithm is improved for multilabel learning by using correlations among labels (CML-kNN). The CML- kNN algorithm first exploits the dependence between every two labels to update the origin label matrix and then performs multilabel learning to estimate the probabilities of labels by using the integrated features. Finally, it recommends the top N diseases to the physicians. Experimental results on real health medical data establish the effectiveness and practicability of the proposed CDS framework.



Standards related to Nearest Neighbor Methods

Back to Top

No standards are currently tagged "Nearest Neighbor Methods"


Jobs related to Nearest Neighbor Methods

Back to Top