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Articles Transmit / Received Beamforming for Frequency Diverse Array with Symmetrical frequency offsets Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 1-6 (2017); View Description Detailed Analysis of Amplitude and Slope Diffraction Coefficients for knife-edge structure in S-UTD-CH Model Eray Arik, Mehmet Baris Tabakcioglu Adv. Sci. Technol. Eng. Syst. J. 2(3), 7-11 (2017); View Description Applications of Case Based Organizational Memory Supported by the PAbMM Architecture Martín, María de los
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2017
Advances in Science, Technology and Engineering Systems
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Considering all of the graphical illustrations, we do like to conclude that, graphene will be a successor in post silicon era and bring revolutionary changes in the field of fabrication technology. ...
In the end, effect of channel length on device performance has been justified. Variation of effective mobility and minimum carrier density with respect to channel length has also been observed. ...
We also determined the effective mobility of charge carriers and minimum carrier concentration in the channel according to channel length. Both illustration yields satisfactory outcomes. ...
doi:10.25046/aj0203177
fatcat:qhd2rteuajdqzo77fq74ixwevu
The effectiveness of using diversity to select multiple classifier systems with varying classification thresholds
2018
Journal of Algorithms & Computational Technology
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Historically, classifier ensemble accuracy has been used to select which pattern recognition algorithms are included in a multiple classifier system. ...
Using a wide range of classification data sets, methodologies, and fusion techniques, current diversity research is extended by expanding classifier domains before employing fusion methodologies. ...
Introduction There is considerable effort in the pattern recognition field to combine the outputs of individual classifiers to create a multiple classifier system (MCS), also termed an "ensemble," which ...
doi:10.1177/1748301818761132
fatcat:samr6inmzvh5fmldqbt44rl4vi
Diversity in multiple classifier ensembles based on binary feature quantisation with application to face recognition
2008
Applied Soft Computing
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In this paper we present two methods to create multiple classifier systems based on an initial transformation of the original features to the binary domain and subsequent decompositions (quantisation). ...
Rather, some type of trade off seems to be necessary between participant classifiers' accuracy and ensemble diversity in order to achieve maximum recognition gains. # ...
Acknowledgement The authors gratefully acknowledge the support of the UK Engineering and Physical Sciences Research Council (EPSRC). ...
doi:10.1016/j.asoc.2005.08.002
fatcat:k6cb5c35r5eqhfkzxwx462qnbe
A Learning Scheme for Microgrid Islanding and Reconnection
[article]
2017
arXiv
pre-print
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A dynamics simulator fed with pre-acquired system parameters is used to create training data for the SVM in various operating states. ...
A support vector machine (SVM) leveraging real-time data from phasor measurement units (PMUs) is proposed to predict in real time whether the reconnection of a sub-network to the main grid would lead to ...
Section II gives a brief background of Support Vector Machines (SVM). Section III covers the methodology to create a power system classifier. ...
arXiv:1611.05317v2
fatcat:jprvtodhnnhk3azq3tewvi65s4
Classifier Selection Based on the Correlation of Diversity Measures: When Fewer Is More
2013
2013 XXVI Conference on Graphics, Patterns and Images
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Diversity measures are used to rank pairs of classifiers and the agreement among ranked lists guides the classifier selection process. ...
Examples of successful initiatives are those dedicated to the development of learning techniques for data fusion or Multiple Classifier Systems (MCS). ...
[10] , for example, proposed an innovative and effective ensemble system by using meta-learning based on support vector machines. ...
doi:10.1109/sibgrapi.2013.12
dblp:conf/sibgrapi/FariaSSRT13
fatcat:ippk2zat6zg4lndawp42lmsace
FAKE STATEMENTS DETECTION WITH ENSEMBLE OF MACHINE LEARNING ALGORITHMS
2018
Problems of Information Technology
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It is suggested to use number of different machine learning techniques for that and uniting them to a single system (ensemble) which predicts probability that given statement is true or not and performs ...
The paper is devoted to an attempt of classifying statements made by public figures as true or false (fake). ...
This makes a data set useful for creating a system which will classify statements as true or false. ...
doi:10.25045/jpit.v09.i2.06
fatcat:c46u2xfshjfsjpzl62hbupdsge
Wagging for Combining Weighted One-class Support Vector Machines
2015
Procedia Computer Science
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A weighted version of boosting is used, and the output weights for each object are used directly in the process of training Weighted One-Class Support Vector Machines. ...
This introduces a diversity into the pool of one-class classifiers and extends the competence of formed ensemble. ...
Details of the chosen data sets are given in Table 1 .
Set-up For the experiment a Weighted One-Class Support Vector Machine with a RBF kernel is used as a base classifier. ...
doi:10.1016/j.procs.2015.05.351
fatcat:uvdzwt2nfrdltf4tk5gctfd6g4
Multi-sensor fusion based on multiple classifier systems for human activity identification
2019
Human-Centric Computing and Information Sciences
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In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection ...
The benefit of the proposed multisensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. ...
Acknowledgements The authors would like to thank University of Malaya for sponsoring the paper through the BKP Special grants and researchers that collected the datasets that were used to support this ...
doi:10.1186/s13673-019-0194-5
fatcat:oif3o7dfhzdwhcqeept7t5jypq
Creating Ensemble Classifiers with Information Entropy Diversity Measure
2021
Security and Communication Networks
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One of the major problems in creating ensemble classifiers is the classification accuracy and diversity of the component classifiers. ...
In this algorithm, information entropy is introduced to measure the diversity of component classifiers, and a cyclic iterative optimization selection tactic is applied to select component classifiers from ...
, or interpretation of data; the writing of the manuscript, or the decision to publish the results. ...
doi:10.1155/2021/9953509
fatcat:spm2o7nnc5hbji6jnetd42ntxq
Semantics-based Web service classification using morphological analysis and ensemble learning techniques
2016
International Journal of Data Science and Analytics
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Using these feature vector models, services are classified as per their domain, using ensemble machine learning methods. ...
To capture the functional diversity of the services, different feature vector selection techniques are used to represent a service in vector space, with the aim of finding the optimal set of features. ...
Support Vector Machine (SVM) [15] is a supervised learning method that uses the concept of hyperplanes for classifying high-dimensional data. ...
doi:10.1007/s41060-016-0026-x
dblp:journals/ijdsa/KamathA16
fatcat:yvigqa2tcrdgdltk3vuwtappki
A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data
2013
Journal of the Indian Society of Remote Sensing
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This paper presents a new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system. ...
Referring to the limitation of single classifier in these situations, Multiple Classifier Systems (MCS) may have better performance than single classifier. ...
It means that multiple classifier system for the AVIRIS data set exhibits higher diversity and lower correlation measures in comparison ROSIS data. ...
doi:10.1007/s12524-013-0286-z
fatcat:xlbisnt5wjdanpjye4asmbkp4a
A New Diversity Technique for Imbalance Learning Ensembles
2018
International Journal of Engineering & Technology
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Classifier Ensembles is a method often used in overcoming class imbalance problems. Data Diversity is one of the cornerstones of ensembles. ...
This study shows that the data diversity is related to performance in the imbalance learning ensembles and the proposed methods can obtain better data diversity. ...
Acknowledgement This work was supported by the Grant of Ministry of Research, Technology, and Higher Education (KEMENRISTEKDIKTI) of the Republic of Indonesia. ...
doi:10.14419/ijet.v7i2.11251
fatcat:u7lywd7gajgsfo45oahocqdicq
Multiple Video Instance Detection and Retrieval using Spatio-Temporal Analysis using Semi Supervised SVM Algorithm
2017
International Journal of Computer Applications
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the present finish, the work to formulate this drawback because the spatio-temporal trajectory search downside, wherever a trajectory may be a sequence of bounding boxes that find the thing instance in ...
The work tends to solve the key bottleneck in applying the approach to object instance search by leverage a randomized approach to change quick marking of any bounding boxes within the video volume. ...
Geographic knowledge discovery using diverse data types GKD methods should be developed that can handle diverse data types beyond the traditional raster and vector models, including imagery and geo-referenced ...
doi:10.5120/ijca2017913495
fatcat:ozzueddgjnbcfhhmp3stikmpjq
Logo Recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers
[chapter]
2013
Lecture Notes in Computer Science
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The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. ...
Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features ...
Creating an ensemble of classifiers There are three general approaches to creating an ensemble of classifiers in stateof-the-art research, which can be considered as different ways to achieve diversity ...
doi:10.1007/978-3-642-38457-8_1
fatcat:b3v5mmqnk5g27jog5seylp6tgm
Multiple Classifier Systems for Embedded String Patterns
[chapter]
2006
Lecture Notes in Computer Science
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We present methods for combining multiple classifiers trained on various vectorial data representations. As base classifiers we use nearest neighbor methods and support vector machine. ...
Multiple classifier systems are a well proven and tested instrument for enhancing the recognition accuracy in statistical pattern recognition problems. ...
Conclusions In the present paper we propose a method for creating multiple classifier systems for string patterns. ...
doi:10.1007/11829898_16
fatcat:wpnunok5fjc7nm3r3ml2ef4rwe
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