Conferences related to Classification algorithms

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting

The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science


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 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.


2020 IEEE Power & Energy Society General Meeting (PESGM)

The Annual IEEE PES General Meeting will bring together over 2900 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutorials and more


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Periodicals related to Classification algorithms

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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.


Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


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.


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


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Most published Xplore authors for Classification algorithms

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Xplore Articles related to Classification algorithms

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Improved Unsupervised Classification Based on Freeman-Durden Polarimetric Decomposition

7th European Conference on Synthetic Aperture Radar, 2008

An improved unsupervised classification algorithm based on Freeman-Durden decomposition is presented. We survey the four different combinations of three basic scattering mechanisms by introducing the scattering power entropy and anisotropy parameter. Classification result on NASA/JPL AIRSAR L-band PolSAR data demonstrates the effectiveness of this algorithm.


Reducing inconsistent rules based on irregular decision table

Tsinghua Science and Technology, 2004

In this paper, we study the problem of rule extraction from data sets using the rough set method. For inconsistent rules due to improper selection of split-points during discretization, and/or to lack of information, we propose two methods to remove their inconsistency based on irregular decision tables. By using these methods, inconsistent rules are eliminated as far as possible, without ...


Non-Independent term selection for Chinese text categorization

Tsinghua Science and Technology, 2009

Chinese text categorization differs from English text categorization due to its much larger term set (of words or character n-grams), which results in very slow training and working of modern high-performance classifiers. This study assumes that this high-dimensionality problem is related to the redundancy in the term set, which cannot be solved by traditional term selection methods. A greedy algorithm ...


II Many Ways to Learn

The Deep Learning Revolution, None

None


Immune Concentration-Based Malware Detection Approaches

Artificial Immune System: Applications in Computer Security, None

This chapter applies the immune concentration-based malware detection approaches. The local concentration-based malware detection method connects a certain number of two element local concentration vectors as the feature vector. To achieve better detection performance, particle swarm optimization (PSO) is used to optimize the parameters of local concentration. Then the hybrid concentration-based feature extraction (HCFE) approach is presented by extracting the ...


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Educational Resources on Classification algorithms

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IEEE.tv Videos

Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
Computing Based on Material Training: Application to Binary Classification Problems - IEEE Rebooting Computing 2017
Solving Sparse Representation for Image Classification using Quantum D-Wave 2X Machine - IEEE Rebooting Computing 2017
ICASSP 2012 Plenary-Dr. Stephane Mallat
IMS 2012 Microapps - Custom OFDM Validation of Wireless/Military DSP Algorithms and RF Components Daren McClearnon, Jin-Biao Xu, Agilent EEsof
Collaborative Filtering II
Learning with Kernels for Streams of Structured Data
Parallelized Linear Classification with Volumetric Chemical Perceptrons - Jacob Rosenstein - ICRC 2018
Unconventional Superconductivity: From History to Mystery
Hyperdimensional Biosignal Processing: A Case Study for EMG-based Hand Gesture Recognition - Abbas Rahimi: 2016 International Conference on Rebooting Computing
Cultural Algorithms: Harnessing the Power of Social Intelligence 1
Optimization Algorithms for Signal Processing
Cultural Algorithms: Harnessing the Power of Social Intelligence 2
Comparing Partitions from Clustering Algorithms
Life Through the Eyes of a Machine
Some Thoughts on a Gap Between Theory and Practice of Evolutionary Algorithms - WCCI 2012
Spiking Network Algorithms for Scientific Computing - William Severa: 2016 International Conference on Rebooting Computing
Eva Tardos - IEEE John von Neumann Medal, 2019 IEEE Honors Ceremony
Search Techniques
Multiple Sensor Fault Detection and Isolation in Complex Distributed Dynamical Systems

IEEE-USA E-Books

  • Improved Unsupervised Classification Based on Freeman-Durden Polarimetric Decomposition

    An improved unsupervised classification algorithm based on Freeman-Durden decomposition is presented. We survey the four different combinations of three basic scattering mechanisms by introducing the scattering power entropy and anisotropy parameter. Classification result on NASA/JPL AIRSAR L-band PolSAR data demonstrates the effectiveness of this algorithm.

  • Reducing inconsistent rules based on irregular decision table

    In this paper, we study the problem of rule extraction from data sets using the rough set method. For inconsistent rules due to improper selection of split-points during discretization, and/or to lack of information, we propose two methods to remove their inconsistency based on irregular decision tables. By using these methods, inconsistent rules are eliminated as far as possible, without affecting the remaining consistent rules. Experimental test indicates that use of the new method leads to an improvement in the mean accuracy of the extracted rules.

  • Non-Independent term selection for Chinese text categorization

    Chinese text categorization differs from English text categorization due to its much larger term set (of words or character n-grams), which results in very slow training and working of modern high-performance classifiers. This study assumes that this high-dimensionality problem is related to the redundancy in the term set, which cannot be solved by traditional term selection methods. A greedy algorithm framework named “non-independent term selection” is presented, which reduces the redundancy according to string- level correlations. Several preliminary implementations of this idea are demonstrated. Experiment results show that a good tradeoff can be reached between the performance and the size of the term set.

  • II Many Ways to Learn

    None

  • Immune Concentration-Based Malware Detection Approaches

    This chapter applies the immune concentration-based malware detection approaches. The local concentration-based malware detection method connects a certain number of two element local concentration vectors as the feature vector. To achieve better detection performance, particle swarm optimization (PSO) is used to optimize the parameters of local concentration. Then the hybrid concentration-based feature extraction (HCFE) approach is presented by extracting the hybrid concentration (HC) of malware in both the global resolution and the local resolution. Self detector library are composed of detectors with maximum representative of benign files and nonself detector library are composed of those detectors with maximum representative of malware. Several robust optimization approaches can be employed to optimize the input vector, like PSO and genetic algorithm (GA). The hybrid concentration-based feature extraction approach extracts the hybrid concentration of a sample in both the global resolution and the local resolution.

  • Robust recursive AR speech analysis based on quadratic classifier with sliding training data set and a heuristic decision threshold

    A robust recursive procedure for identification of nonstationary AR speech model based on a quadratic classifier with a heuristic decision threshold is proposed and evaluated. A comparative experimental analysis is done through processing natural speech signal with voiced and mixed excitation segments. Obtained results show that the proposed robust procedure based on the quadratic classifier with sliding training data set and the heuristic decision threshold achieves more accurate AR speech parameter estimation and provides improved tracking performance.

  • Fuzzy Clustering and Classification

    This chapter focuses fuzzy clustering with the fuzzy C-means (FCM) and the possibilistic C-means (PCM) as the primary examples. Clustering methods are used to look for structure in sets of unlabeled vectors. In many applications, we need to assign known labels to test data. In these cases, we assume that we have training sets of patterns that represent the various classes under consideration. The chapter shows how both the distances and fuzzy labels are combined to create class labels for the test vector. Like the crisp counterpart, the fuzzy k-nearest neighbor (FKNN) algorithm is simple in concept. The concept of nearest neighbors normally refers to distance in a metric space. In Gader et al., the k-NN soft labels were used to drive a self- organizing feature map (SOFM), a neural-based clustering algorithm. It can be considered the extreme case of the multiprototype classification that is related to clustering.

  • A Constructive Neural Network Learning Method Based on Quotient Space and Its Application in Coal Mine Gas Prediction

    This paper uses constructive neural network learning approach to predict gas concentrations, under the framework of quotient space granular computing model. Using quotient space granular computing theory, the problem can be macro-level analysis - examining different particle size between the quotient space conversion, movement, interdependent relations, and the original features of the database information to build grain size, using a variety of granularity, from different levels of analysis of complex gas data makes the learning characteristics of the sample is more obvious, in order to better meet the requirements of machine learning. Constructive neural network learning method achieves the data mining of different particle size structure the quotient space from the micro. At last, the method is applied to predict gas concentration, and the satisfying results are achieved. It is expected that Constructive Neural Network Learning Method will have wide applications.

  • Group locality preserving orthogonal nonnegative matrix factorization

    Non-negative matrix factorization (NMF) is useful in finding basis information of non-negative data. It is a new dimension reduction method. In this paper, a Group Locality Preserving Orthogonal Nonnegative Matrix Factorization (GLPONMF) is investigated. The idea is to extend the NMF method in order to extract basis vectors for each sample class and at the same time enforce the locality preserving orthogonal properties. Finally, the experimental evaluation has been given.

  • A heuristic-based band selection approach to improve classification accuracy in hyperspectral images

    Variable Neighborhood Search (VNS) is one of the methods, called metaheuristic, which are based on searching the solution space quickly to get optimal or approximately optimal solution. This method is based on the systematically neighborhood change in search area and generally used to achieve the optimal solution in a short time in high dimensional search space. Examining the data including large scale of information such as hyperspectral images and eliminating redundant features (bands) is quite important for computation time and target classification/detection performance. In this study, band selection as a dimension reduction procedure is employed to hyperspectral images using VNS method. Then the classification was done for different selections of the spectral bands with the spectral angle mapper (SAM) and support vector machine (SVM) on hyperspectral Indian Pine image. The experimental results show that the VNS-based dimension reduction algorithm can improve classification performance in high dimensional hyperspectral data.



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