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Pattern recognition and classification (MLR-PATT)

CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING

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Authors:
Yongjian Xue, Pierre Beauseroy
Submitted On:
24 April 2018 - 4:44am
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Poster_Yongjian_ICASSP.pdf

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[1] Yongjian Xue, Pierre Beauseroy, "CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3164. Accessed: May. 25, 2018.
@article{3164-18,
url = {http://sigport.org/3164},
author = {Yongjian Xue; Pierre Beauseroy },
publisher = {IEEE SigPort},
title = {CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING},
year = {2018} }
TY - EJOUR
T1 - CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING
AU - Yongjian Xue; Pierre Beauseroy
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3164
ER -
Yongjian Xue, Pierre Beauseroy. (2018). CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING. IEEE SigPort. http://sigport.org/3164
Yongjian Xue, Pierre Beauseroy, 2018. CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING. Available at: http://sigport.org/3164.
Yongjian Xue, Pierre Beauseroy. (2018). "CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING." Web.
1. Yongjian Xue, Pierre Beauseroy. CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3164

CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING

Paper Details

Authors:
Yongjian Xue, Pierre Beauseroy
Submitted On:
24 April 2018 - 4:44am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster_Yongjian_ICASSP.pdf

(21 downloads)

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[1] Yongjian Xue, Pierre Beauseroy, "CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3163. Accessed: May. 25, 2018.
@article{3163-18,
url = {http://sigport.org/3163},
author = {Yongjian Xue; Pierre Beauseroy },
publisher = {IEEE SigPort},
title = {CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING},
year = {2018} }
TY - EJOUR
T1 - CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING
AU - Yongjian Xue; Pierre Beauseroy
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3163
ER -
Yongjian Xue, Pierre Beauseroy. (2018). CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING. IEEE SigPort. http://sigport.org/3163
Yongjian Xue, Pierre Beauseroy, 2018. CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING. Available at: http://sigport.org/3163.
Yongjian Xue, Pierre Beauseroy. (2018). "CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING." Web.
1. Yongjian Xue, Pierre Beauseroy. CONSTANT FALSE ALARM RATE FOR ONLINE ONE CLASS SVM LEARNING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3163

Clustering of data with missing entries


The analysis of large datasets is often complicated by the presence of missing entries, mainly because most of the current machine learning algorithms are designed to work with full data. The main focus of this work is to introduce a clustering

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Authors:
Sunrita Poddar, Mathews Jacob
Submitted On:
14 April 2018 - 8:11pm
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clusteringMissingEntries

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icassp18poster.pdf

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[1] Sunrita Poddar, Mathews Jacob, "Clustering of data with missing entries", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2858. Accessed: May. 25, 2018.
@article{2858-18,
url = {http://sigport.org/2858},
author = {Sunrita Poddar; Mathews Jacob },
publisher = {IEEE SigPort},
title = {Clustering of data with missing entries},
year = {2018} }
TY - EJOUR
T1 - Clustering of data with missing entries
AU - Sunrita Poddar; Mathews Jacob
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2858
ER -
Sunrita Poddar, Mathews Jacob. (2018). Clustering of data with missing entries. IEEE SigPort. http://sigport.org/2858
Sunrita Poddar, Mathews Jacob, 2018. Clustering of data with missing entries. Available at: http://sigport.org/2858.
Sunrita Poddar, Mathews Jacob. (2018). "Clustering of data with missing entries." Web.
1. Sunrita Poddar, Mathews Jacob. Clustering of data with missing entries [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2858

TV-SVM: Support Vector Machine with Total Variational Regularization


To leverage the spatial relationship of lattice data, such as images, we introduce total variational (TV) regularization into support vector machines (SVM), called TV-SVM. TV-SVM encourages local smoothness and sparsity in gradient domain of the learned parameters. TV-SVM is optimized via the alternating direction method of multipliers (ADMM) algorithm and is significantly better than (Linear) SVM for image classifications.

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Authors:
Zhendong Zhang,Cheolkon Jung
Submitted On:
13 April 2018 - 12:30pm
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ICASSP2018poster_TVSVM_final.pdf

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[1] Zhendong Zhang,Cheolkon Jung, "TV-SVM: Support Vector Machine with Total Variational Regularization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2728. Accessed: May. 25, 2018.
@article{2728-18,
url = {http://sigport.org/2728},
author = {Zhendong Zhang;Cheolkon Jung },
publisher = {IEEE SigPort},
title = {TV-SVM: Support Vector Machine with Total Variational Regularization},
year = {2018} }
TY - EJOUR
T1 - TV-SVM: Support Vector Machine with Total Variational Regularization
AU - Zhendong Zhang;Cheolkon Jung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2728
ER -
Zhendong Zhang,Cheolkon Jung. (2018). TV-SVM: Support Vector Machine with Total Variational Regularization. IEEE SigPort. http://sigport.org/2728
Zhendong Zhang,Cheolkon Jung, 2018. TV-SVM: Support Vector Machine with Total Variational Regularization. Available at: http://sigport.org/2728.
Zhendong Zhang,Cheolkon Jung. (2018). "TV-SVM: Support Vector Machine with Total Variational Regularization." Web.
1. Zhendong Zhang,Cheolkon Jung. TV-SVM: Support Vector Machine with Total Variational Regularization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2728

TV-SVM: Support Vector Machine with Total Variational Regularization


To leverage the spatial relationship of lattice data, such as images, we introduce total variational (TV) regularization into support vector machines (SVM), called TV-SVM. TV-SVM encourages local smoothness and sparsity in gradient domain of the learned parameters. TV-SVM is optimized via the alternating direction method of multipliers (ADMM) algorithm and is significantly better than (Linear) SVM for image classifications.

Paper Details

Authors:
Zhendong Zhang,Cheolkon Jung
Submitted On:
13 April 2018 - 12:22pm
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ICASSP2018poster_TVSVM_final.pdf

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[1] Zhendong Zhang,Cheolkon Jung, "TV-SVM: Support Vector Machine with Total Variational Regularization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2724. Accessed: May. 25, 2018.
@article{2724-18,
url = {http://sigport.org/2724},
author = {Zhendong Zhang;Cheolkon Jung },
publisher = {IEEE SigPort},
title = {TV-SVM: Support Vector Machine with Total Variational Regularization},
year = {2018} }
TY - EJOUR
T1 - TV-SVM: Support Vector Machine with Total Variational Regularization
AU - Zhendong Zhang;Cheolkon Jung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2724
ER -
Zhendong Zhang,Cheolkon Jung. (2018). TV-SVM: Support Vector Machine with Total Variational Regularization. IEEE SigPort. http://sigport.org/2724
Zhendong Zhang,Cheolkon Jung, 2018. TV-SVM: Support Vector Machine with Total Variational Regularization. Available at: http://sigport.org/2724.
Zhendong Zhang,Cheolkon Jung. (2018). "TV-SVM: Support Vector Machine with Total Variational Regularization." Web.
1. Zhendong Zhang,Cheolkon Jung. TV-SVM: Support Vector Machine with Total Variational Regularization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2724

TV-SVM: Support Vector Machine with Total Variational Regularization


To leverage the spatial relationship of lattice data, such as images, we introduce total variational (TV) regularization into support vector machines (SVM), called TV-SVM. TV-SVM encourages local smoothness and sparsity in gradient domain of the learned parameters. TV-SVM is optimized via the alternating direction method of multipliers (ADMM) algorithm and is significantly better than (Linear) SVM for image classifications.

Paper Details

Authors:
Zhendong Zhang,Cheolkon Jung
Submitted On:
13 April 2018 - 12:22pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2018poster_TVSVM_final.pdf

(19 downloads)

Keywords

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[1] Zhendong Zhang,Cheolkon Jung, "TV-SVM: Support Vector Machine with Total Variational Regularization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2623. Accessed: May. 25, 2018.
@article{2623-18,
url = {http://sigport.org/2623},
author = {Zhendong Zhang;Cheolkon Jung },
publisher = {IEEE SigPort},
title = {TV-SVM: Support Vector Machine with Total Variational Regularization},
year = {2018} }
TY - EJOUR
T1 - TV-SVM: Support Vector Machine with Total Variational Regularization
AU - Zhendong Zhang;Cheolkon Jung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2623
ER -
Zhendong Zhang,Cheolkon Jung. (2018). TV-SVM: Support Vector Machine with Total Variational Regularization. IEEE SigPort. http://sigport.org/2623
Zhendong Zhang,Cheolkon Jung, 2018. TV-SVM: Support Vector Machine with Total Variational Regularization. Available at: http://sigport.org/2623.
Zhendong Zhang,Cheolkon Jung. (2018). "TV-SVM: Support Vector Machine with Total Variational Regularization." Web.
1. Zhendong Zhang,Cheolkon Jung. TV-SVM: Support Vector Machine with Total Variational Regularization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2623

Scattering Features for Multimodal Gait Recognition


Gait.pdf

PDF icon Gait.pdf (106 downloads)

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Authors:
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton
Submitted On:
25 November 2017 - 8:19pm
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Gait.pdf

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[1] Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, "Scattering Features for Multimodal Gait Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2369. Accessed: May. 25, 2018.
@article{2369-17,
url = {http://sigport.org/2369},
author = {Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton },
publisher = {IEEE SigPort},
title = {Scattering Features for Multimodal Gait Recognition},
year = {2017} }
TY - EJOUR
T1 - Scattering Features for Multimodal Gait Recognition
AU - Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2369
ER -
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). Scattering Features for Multimodal Gait Recognition. IEEE SigPort. http://sigport.org/2369
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, 2017. Scattering Features for Multimodal Gait Recognition. Available at: http://sigport.org/2369.
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). "Scattering Features for Multimodal Gait Recognition." Web.
1. Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. Scattering Features for Multimodal Gait Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2369

Scattering Features for Multimodal Gait Recognition


Gait.pdf

PDF icon Gait.pdf (90 downloads)

Paper Details

Authors:
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton
Submitted On:
25 November 2017 - 8:19pm
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Gait.pdf

(90 downloads)

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[1] Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, "Scattering Features for Multimodal Gait Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2368. Accessed: May. 25, 2018.
@article{2368-17,
url = {http://sigport.org/2368},
author = {Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton },
publisher = {IEEE SigPort},
title = {Scattering Features for Multimodal Gait Recognition},
year = {2017} }
TY - EJOUR
T1 - Scattering Features for Multimodal Gait Recognition
AU - Srdan Kitic;Gilles Puy;Patrick Perez;Philippe Gilberton
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2368
ER -
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). Scattering Features for Multimodal Gait Recognition. IEEE SigPort. http://sigport.org/2368
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton, 2017. Scattering Features for Multimodal Gait Recognition. Available at: http://sigport.org/2368.
Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. (2017). "Scattering Features for Multimodal Gait Recognition." Web.
1. Srdan Kitic,Gilles Puy,Patrick Perez,Philippe Gilberton. Scattering Features for Multimodal Gait Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2368

Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides


Scene text detection is a critical prerequisite for many fascinating applications for vision-based intelligent robots. Existing methods detect texts either using the local information only or casting it as a semantic segmentation problem. They tend to produce a large number of false alarms or cannot separate individual words accurately. In this work, we present an elegant segmentation-aided text detection solution that predicts the word-level bounding boxes using an end-to-end trainable deep convolutional neural network.

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Authors:
Junting Zhang, Yuewei Na, Siyang Li, C.-C. Jay Kuo
Submitted On:
12 April 2018 - 5:30pm
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GlobalSIP17_Oral_Segmentation-aided_Text_Detection

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[1] Junting Zhang, Yuewei Na, Siyang Li, C.-C. Jay Kuo, "Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2354. Accessed: May. 25, 2018.
@article{2354-17,
url = {http://sigport.org/2354},
author = {Junting Zhang; Yuewei Na; Siyang Li; C.-C. Jay Kuo },
publisher = {IEEE SigPort},
title = {Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides },
year = {2017} }
TY - EJOUR
T1 - Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides
AU - Junting Zhang; Yuewei Na; Siyang Li; C.-C. Jay Kuo
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2354
ER -
Junting Zhang, Yuewei Na, Siyang Li, C.-C. Jay Kuo. (2017). Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides . IEEE SigPort. http://sigport.org/2354
Junting Zhang, Yuewei Na, Siyang Li, C.-C. Jay Kuo, 2017. Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides . Available at: http://sigport.org/2354.
Junting Zhang, Yuewei Na, Siyang Li, C.-C. Jay Kuo. (2017). "Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides ." Web.
1. Junting Zhang, Yuewei Na, Siyang Li, C.-C. Jay Kuo. Efficient Segmentation-Aided Text Detection for Intelligent Robots_Slides [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2354

A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury


Assistive technologies such as wheelchairs, canes, and walkers have significantly improved the mobility, function, and quality of life for individuals with spinal cord injury (SCI). In this article, we propose a framework which combines machine learning algorithms with wearable sensors to capture and track mobility in individuals with SCI. Pilot testing in two individuals without SCI indicated that four to seven features obtained from sensors worn on the body or placed on the assistive technology could successfully detect mobility and mobility modes.

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Authors:
Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath
Submitted On:
13 November 2017 - 10:28am
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MobilityFramework_0.5_PDF.pdf

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[1] Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath, "A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2337. Accessed: May. 25, 2018.
@article{2337-17,
url = {http://sigport.org/2337},
author = {Amir Mohammad Amiri; Noor Shoaib; Shivayogi V. Hiremath },
publisher = {IEEE SigPort},
title = {A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury},
year = {2017} }
TY - EJOUR
T1 - A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury
AU - Amir Mohammad Amiri; Noor Shoaib; Shivayogi V. Hiremath
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2337
ER -
Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath. (2017). A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury. IEEE SigPort. http://sigport.org/2337
Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath, 2017. A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury. Available at: http://sigport.org/2337.
Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath. (2017). "A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury." Web.
1. Amir Mohammad Amiri, Noor Shoaib, Shivayogi V. Hiremath. A Framework to Enhance Assistive Technology-based Mobility Tracking in Individuals with Spinal Cord Injury [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2337

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