The Experts below are selected from a list of 76344 Experts worldwide ranked by ideXlab platform
Daniel Dementhon - One of the best experts on this subject based on the ideXlab platform.
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Hybrid independent component analysis and support vector machine Learning Scheme for face detection
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001Co-Authors: Yuan Qi, David Doermann, Daniel DementhonAbstract:We propose a new hybrid unsupervised/supervised Learning Scheme that integrates independent component analysis (ICA) with the support vector machine (SVM) approach and apply this new Learning Scheme to the face detection problem. In low-level feature extraction, ICA produces independent image bases that emphasize edge information in the image data. In high-level classification, SVM classifies the ICA features as a face or non-faces. Our experimental results show that by using ICA features we obtain a larger margin of separation and fewer support vectors than by training SVM directly on the image data. This indicates better generalization performance, which is verified in our experiments.
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ICASSP - Hybrid independent component analysis and support vector machine Learning Scheme for face detection
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001Co-Authors: Yuan Qi, David Doermann, Daniel DementhonAbstract:We propose a new hybrid unsupervised/supervised Learning Scheme that integrates independent component analysis (ICA) with the support vector machine (SVM) approach and apply this new Learning Scheme to the face detection problem. In low-level feature extraction, ICA produces independent image bases that emphasize edge information in the image data. In high-level classification, SVM classifies the ICA features as a face or non-faces. Our experimental results show that by using ICA features we obtain a larger margin of separation and fewer support vectors than by training SVM directly on the image data. This indicates better generalization performance, which is verified in our experiments.
Chen Hoffmann - One of the best experts on this subject based on the ideXlab platform.
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Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine Learning Scheme
Neuroradiology, 2019Co-Authors: Shai Shrot, Moshe Salhov, Nir Dvorski, Eli Konen, Amir Averbuch, Chen HoffmannAbstract:Purpose While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine Learning Scheme using basic and advanced MR sequences for distinguishing different types of brain tumors. Methods The study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma). A computer-assisted classification Scheme, combining morphologic MRI, perfusion MRI, and DTI metrics, was developed and used for tumor classification. The proposed multistep Scheme consists of pre-processing, ROI definition, features extraction, feature selection, and classification. Feature subset selection was performed using support vector machines (SVMs). Classification performance was assessed by leave-one-out cross-validation. Given an ROI, the entire classification process was done automatically via computer and without any human intervention. Results A binary hierarchical classification tree was chosen. In the first step, selected features were chosen for distinguishing glioblastoma from the remaining three classes, followed by separation of meningioma from metastasis and PCNSL, and then to discriminate PCNSL from metastasis. The binary SVM classification accuracy, sensitivity and specificity for glioblastoma, metastasis, meningiomas, and primary central nervous system lymphoma were 95.7, 81.6, and 91.2%; 92.7, 95.1, and 93.6%; 97, 90.8, and 58.3%; and 91.5, 90, and 96.9%, respectively. Conclusion A machine Learning Scheme using data from anatomical and advanced MRI sequences resulted in high-performance automatic tumor classification algorithm. Such a Scheme can be integrated into clinical decision support systems to optimize tumor classification.
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application of mr morphologic diffusion tensor and perfusion imaging in the classification of brain tumors using machine Learning Scheme
Neuroradiology, 2019Co-Authors: Moshe Salhov, Nir Dvorski, Eli Konen, Shai Shrot, Chen Hoffmann, Amir AverbuchAbstract:Purpose While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine Learning Scheme using basic and advanced MR sequences for distinguishing different types of brain tumors.
Tunyu Chang - One of the best experts on this subject based on the ideXlab platform.
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an adaptive Learning Scheme for load balancing with zone partition in multi sink wireless sensor network
Expert Systems With Applications, 2012Co-Authors: Shengtzong Cheng, Tunyu ChangAbstract:Highlights? We propose an adaptive Learning Scheme for load balancing Scheme in multi-sink WSN. ? The mobile anchor adaptively partitions the network into several zones. ? The agent learns to balance the load in the way of reallocate the collection zones. ?The agent applies the residual energy of hotspots around sink nodes. ?The proposed QAZP Scheme prolongs the lifetime of the wireless sensor network. In many researches on load balancing in multi-sink WSN, sensors usually choose the nearest sink as destination for sending data. However, in WSN, events often occur in specific area. If all sensors in this area all follow the nearest-sink strategy, sensors around nearest sink called hotspot will exhaust energy early. It means that this sink is isolated from network early and numbers of routing paths are broken. In this paper, we propose an adaptive Learning Scheme for load balancing Scheme in multi-sink WSN. The agent in a centralized mobile anchor with directional antenna is introduced to adaptively partition the network into several zones according to the residual energy of hotspots around sink nodes. In addition, machine Learning is applied to the mobile anchor to make it adaptable to any traffic pattern. Through interactions with the environment, the agent can discovery a near-optimal control policy for movement of mobile anchor. The policy can achieve minimization of residual energy's variance among sinks, which prevent the early isolation of sink and prolong the network lifetime.
Yuan Qi - One of the best experts on this subject based on the ideXlab platform.
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Hybrid independent component analysis and support vector machine Learning Scheme for face detection
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001Co-Authors: Yuan Qi, David Doermann, Daniel DementhonAbstract:We propose a new hybrid unsupervised/supervised Learning Scheme that integrates independent component analysis (ICA) with the support vector machine (SVM) approach and apply this new Learning Scheme to the face detection problem. In low-level feature extraction, ICA produces independent image bases that emphasize edge information in the image data. In high-level classification, SVM classifies the ICA features as a face or non-faces. Our experimental results show that by using ICA features we obtain a larger margin of separation and fewer support vectors than by training SVM directly on the image data. This indicates better generalization performance, which is verified in our experiments.
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ICASSP - Hybrid independent component analysis and support vector machine Learning Scheme for face detection
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001Co-Authors: Yuan Qi, David Doermann, Daniel DementhonAbstract:We propose a new hybrid unsupervised/supervised Learning Scheme that integrates independent component analysis (ICA) with the support vector machine (SVM) approach and apply this new Learning Scheme to the face detection problem. In low-level feature extraction, ICA produces independent image bases that emphasize edge information in the image data. In high-level classification, SVM classifies the ICA features as a face or non-faces. Our experimental results show that by using ICA features we obtain a larger margin of separation and fewer support vectors than by training SVM directly on the image data. This indicates better generalization performance, which is verified in our experiments.
Shai Shrot - One of the best experts on this subject based on the ideXlab platform.
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Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine Learning Scheme
Neuroradiology, 2019Co-Authors: Shai Shrot, Moshe Salhov, Nir Dvorski, Eli Konen, Amir Averbuch, Chen HoffmannAbstract:Purpose While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine Learning Scheme using basic and advanced MR sequences for distinguishing different types of brain tumors. Methods The study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma). A computer-assisted classification Scheme, combining morphologic MRI, perfusion MRI, and DTI metrics, was developed and used for tumor classification. The proposed multistep Scheme consists of pre-processing, ROI definition, features extraction, feature selection, and classification. Feature subset selection was performed using support vector machines (SVMs). Classification performance was assessed by leave-one-out cross-validation. Given an ROI, the entire classification process was done automatically via computer and without any human intervention. Results A binary hierarchical classification tree was chosen. In the first step, selected features were chosen for distinguishing glioblastoma from the remaining three classes, followed by separation of meningioma from metastasis and PCNSL, and then to discriminate PCNSL from metastasis. The binary SVM classification accuracy, sensitivity and specificity for glioblastoma, metastasis, meningiomas, and primary central nervous system lymphoma were 95.7, 81.6, and 91.2%; 92.7, 95.1, and 93.6%; 97, 90.8, and 58.3%; and 91.5, 90, and 96.9%, respectively. Conclusion A machine Learning Scheme using data from anatomical and advanced MRI sequences resulted in high-performance automatic tumor classification algorithm. Such a Scheme can be integrated into clinical decision support systems to optimize tumor classification.
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application of mr morphologic diffusion tensor and perfusion imaging in the classification of brain tumors using machine Learning Scheme
Neuroradiology, 2019Co-Authors: Moshe Salhov, Nir Dvorski, Eli Konen, Shai Shrot, Chen Hoffmann, Amir AverbuchAbstract:Purpose While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine Learning Scheme using basic and advanced MR sequences for distinguishing different types of brain tumors.
