The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform
Derek T. Anderson - One of the best experts on this subject based on the ideXlab platform.
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SSCI - Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels
2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019Co-Authors: Xiaoxiao Du, Alina Zare, Derek T. AndersonAbstract:Classifier fusion methods integrate complementary information from multiple classifiers or detectors and can aid remote sensing applications such as target detection and hy-perspectral image analysis. The Choquet integral (CI), param-eterized by fuzzy measures (FMs), has been widely used in the literature as an effective non-linear fusion framework. Standard supervised CI fusion algorithms often require precise ground-truth labels for each Training Data Point, which can be difficult or impossible to obtain for remote sensing Data. Previously, we proposed a Multiple Instance Choquet Integral (MICI) classifier fusion approach to address such label uncertainty, yet it can be slow to train due to large search space for FM variables. In this paper, we propose a new efficient learning scheme using binary fuzzy measures (BFMs) with the MICI framework for two-class classifier fusion given ambiguously and imprecisely labeled Training Data. We present experimental results on both synthetic Data and real target detection problems and show that the proposed MICI-BFM algorithm can effectively and efficiently perform classifier fusion given remote sensing Data with imprecise labels.
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Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels
2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019Co-Authors: Xiaoxiao Du, Alina Zare, Derek T. AndersonAbstract:Classifier fusion methods integrate complementary information from multiple classifiers or detectors and can aid remote sensing applications such as target detection and hyperspectral image analysis. The Choquet integral (CI), parameterized by fuzzy measures (FMs), has been widely used in the literature as an effective non-linear fusion framework. Standard supervised CI fusion algorithms often require precise ground-truth labels for each Training Data Point, which can be difficult or impossible to obtain for remote sensing Data. Previously, we proposed a Multiple Instance Choquet Integral (MICI) classifier fusion approach to address such label uncertainty, yet it can be slow to train due to large search space for FM variables. In this paper, we propose a new efficient learning scheme using binary fuzzy measures (BFMs) with the MICI framework for two-class classifier fusion given ambiguously and imprecisely labeled Training Data. We present experimental results on both synthetic Data and real target detection problems and show that the proposed MICI-BFM algorithm can effectively and efficiently perform classifier fusion given remote sensing Data with imprecise labels.
Umesh Vaidya - One of the best experts on this subject based on the ideXlab platform.
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Dynamical system based approach to distributed particle vector machine
2017 American Control Conference (ACC), 2017Co-Authors: Sambarta Dasgupta, Umesh VaidyaAbstract:Classification is at the very center of the supervised learning. In this work, we propose a novel algorithm to classify the test Data set with the aid of a vector field, emanating from the Training Data set. In particular, the vector field is constructed such that the location of each Training Data Point becomes a local minimum of the potential. The test Data Points are allowed to evolve under the influence of the velocity field, generated by the Training Data set, and thereby would be converging to the domain of attractions of different classes. The proposed approach avoids explicit computation of the separating hyper-plane like Support Vector Machine, which becomes difficult, if the structure of the separating hyper-plane is nonlinear. The proposed method is specially suited for online learning problems, as the model Training does not involve any additional time. Comparative simulation studies are presented over Data sets coming from three practical Machine Learning benchmark problems.
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ACC - Dynamical system based approach to distributed particle vector machine
2017 American Control Conference (ACC), 2017Co-Authors: Sambarta Dasgupta, Umesh VaidyaAbstract:Classification is at the very center of the supervised learning. In this work, we propose a novel algorithm to classify the test Data set with the aid of a vector field, emanating from the Training Data set. In particular, the vector field is constructed such that the location of each Training Data Point becomes a local minimum of the potential. The test Data Points are allowed to evolve under the influence of the velocity field, generated by the Training Data set, and thereby would be converging to the domain of attractions of different classes. The proposed approach avoids explicit computation of the separating hyper-plane like Support Vector Machine, which becomes difficult, if the structure of the separating hyper-plane is nonlinear. The proposed method is specially suited for online learning problems, as the model Training does not involve any additional time. Comparative simulation studies are presented over Data sets coming from three practical Machine Learning benchmark problems.
Xiaoxiao Du - One of the best experts on this subject based on the ideXlab platform.
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SSCI - Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels
2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019Co-Authors: Xiaoxiao Du, Alina Zare, Derek T. AndersonAbstract:Classifier fusion methods integrate complementary information from multiple classifiers or detectors and can aid remote sensing applications such as target detection and hy-perspectral image analysis. The Choquet integral (CI), param-eterized by fuzzy measures (FMs), has been widely used in the literature as an effective non-linear fusion framework. Standard supervised CI fusion algorithms often require precise ground-truth labels for each Training Data Point, which can be difficult or impossible to obtain for remote sensing Data. Previously, we proposed a Multiple Instance Choquet Integral (MICI) classifier fusion approach to address such label uncertainty, yet it can be slow to train due to large search space for FM variables. In this paper, we propose a new efficient learning scheme using binary fuzzy measures (BFMs) with the MICI framework for two-class classifier fusion given ambiguously and imprecisely labeled Training Data. We present experimental results on both synthetic Data and real target detection problems and show that the proposed MICI-BFM algorithm can effectively and efficiently perform classifier fusion given remote sensing Data with imprecise labels.
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Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels
2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019Co-Authors: Xiaoxiao Du, Alina Zare, Derek T. AndersonAbstract:Classifier fusion methods integrate complementary information from multiple classifiers or detectors and can aid remote sensing applications such as target detection and hyperspectral image analysis. The Choquet integral (CI), parameterized by fuzzy measures (FMs), has been widely used in the literature as an effective non-linear fusion framework. Standard supervised CI fusion algorithms often require precise ground-truth labels for each Training Data Point, which can be difficult or impossible to obtain for remote sensing Data. Previously, we proposed a Multiple Instance Choquet Integral (MICI) classifier fusion approach to address such label uncertainty, yet it can be slow to train due to large search space for FM variables. In this paper, we propose a new efficient learning scheme using binary fuzzy measures (BFMs) with the MICI framework for two-class classifier fusion given ambiguously and imprecisely labeled Training Data. We present experimental results on both synthetic Data and real target detection problems and show that the proposed MICI-BFM algorithm can effectively and efficiently perform classifier fusion given remote sensing Data with imprecise labels.
Adrijan Baric - One of the best experts on this subject based on the ideXlab platform.
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Sparse ε-tube support vector regression by active learning
Soft Computing, 2013Co-Authors: Vladimir Ceperic, Georges Gielen, Adrijan BaricAbstract:A method for the sparse solution of $$\varepsilon $$ ? -tube support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity ratio and allows the user to adjust the complexity of the resulting models. The sparse representation is guaranteed by limiting the number of Training Data Points for the support vector regression method. Each Training Data Point is selected based on its influence on the accuracy of the model using the active learning principle. The Training time can be adjusted by the user by selecting how often the hyper-parameters of the algorithm are optimised. The advantages of the proposed method are illustrated on several examples. The algorithm performance is compared with the performance of several state-of-the-art algorithms on the well-known benchmark Data sets. The application of the proposed algorithm for the black-box modelling of the electronic circuits is also demonstrated. The experiments clearly show that it is possible to reduce the number of support vectors and significantly improve the accuracy versus complexity ratio of $$\varepsilon $$ ? -tube support vector regression.
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Sparse multikernel support vector regression machines trained by active learning
Expert Systems With Applications, 2012Co-Authors: Vladimir Ceperic, Georges Gielen, Adrijan BaricAbstract:A method for the sparse multikernel support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity ratio and allows the user to adjust the complexity of the resulting models. The sparse representation is guaranteed by limiting the number of Training Data Points for the support vector regression method. Each Training Data Point is selected based on its influence on the accuracy of the model using the active learning principle. A different kernel function is attributed to each Training Data Point, yielding multikernel regressor. The advantages of the proposed method are illustrated on several examples and the experiments show the advantages of the proposed method.
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Recurrent sparse support vector regression machines trained by active learning in the time-domain
Expert Systems With Applications, 2012Co-Authors: Vladimir Ceperic, Georges Gielen, Adrijan BaricAbstract:A method for the sparse solution of recurrent support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity and allows the user to adjust the complexity of the resulting model. The sparse representation is guaranteed by limiting the number of Training Data Points for the support vector regression method. Each Training Data Point is selected based on the accuracy of the fully recurrent model using the active learning principle applied to the successive time-domain Data. The user can adjust the Training time by selecting how often the hyper-parameters of the algorithm should be optimised. The advantages of the proposed method are illustrated on several examples, and the experiments clearly show that it is possible to reduce the number of support vectors and to significantly improve the accuracy versus complexity of recurrent support vector regression machines.
Sambarta Dasgupta - One of the best experts on this subject based on the ideXlab platform.
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Dynamical system based approach to distributed particle vector machine
2017 American Control Conference (ACC), 2017Co-Authors: Sambarta Dasgupta, Umesh VaidyaAbstract:Classification is at the very center of the supervised learning. In this work, we propose a novel algorithm to classify the test Data set with the aid of a vector field, emanating from the Training Data set. In particular, the vector field is constructed such that the location of each Training Data Point becomes a local minimum of the potential. The test Data Points are allowed to evolve under the influence of the velocity field, generated by the Training Data set, and thereby would be converging to the domain of attractions of different classes. The proposed approach avoids explicit computation of the separating hyper-plane like Support Vector Machine, which becomes difficult, if the structure of the separating hyper-plane is nonlinear. The proposed method is specially suited for online learning problems, as the model Training does not involve any additional time. Comparative simulation studies are presented over Data sets coming from three practical Machine Learning benchmark problems.
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ACC - Dynamical system based approach to distributed particle vector machine
2017 American Control Conference (ACC), 2017Co-Authors: Sambarta Dasgupta, Umesh VaidyaAbstract:Classification is at the very center of the supervised learning. In this work, we propose a novel algorithm to classify the test Data set with the aid of a vector field, emanating from the Training Data set. In particular, the vector field is constructed such that the location of each Training Data Point becomes a local minimum of the potential. The test Data Points are allowed to evolve under the influence of the velocity field, generated by the Training Data set, and thereby would be converging to the domain of attractions of different classes. The proposed approach avoids explicit computation of the separating hyper-plane like Support Vector Machine, which becomes difficult, if the structure of the separating hyper-plane is nonlinear. The proposed method is specially suited for online learning problems, as the model Training does not involve any additional time. Comparative simulation studies are presented over Data sets coming from three practical Machine Learning benchmark problems.
