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2012 · 9 papers

Positive semidefinite metric learning using boosting-like algorithms

C. Shen, J. Kim, L. Wang, A. van den Hengel

Citation:
C. Shen, J. Kim, L. Wang, A. van den Hengel. Positive semidefinite metric learning using boosting-like algorithms. Journal of Machine Learning Research. volume: 13, pages: 1007--1036. 2012.
[Code is available at http://code.google.com/p/boosting/]

 @article{JMLR2012Shen,
   author    = "C. Shen and  J. Kim and  L. Wang and  A. {van den Hengel}",
   title     = "Positive semidefinite metric learning using boosting-like algorithms",
   journal   = "Journal of Machine Learning Research",
   volume    = "13",
   pages     = "1007--1036",
   url       = "http://jmlr.csail.mit.edu/papers/v13/shen12a.html",
   year      = "2012",
 }

Fast and robust object detection using asymmetric totally-corrective boosting

P. Wang, C. Shen, N. Barnes, H. Zheng

Citation:
P. Wang, C. Shen, N. Barnes, H. Zheng. Fast and robust object detection using asymmetric totally-corrective boosting. IEEE Transactions on Neural Networks and Learning Systems. volume: 23, number: 1, pages: 33--46. 2012.

 @article{AsymBoost2011Wang,
   author    = "P. Wang and  C. Shen and  N. Barnes and  H. Zheng",
   title     = "Fast and robust object detection using asymmetric totally-corrective boosting",
   journal   = "IEEE Transactions on Neural Networks and Learning Systems",
   volume    = "23",
   number    = "1",
   pages     = "33--46",
   year      = "2012",
 }

UBoost: Boosting with the Universum

C. Shen, P. Wang, F. Shen, H. Wang

Citation:
C. Shen, P. Wang, F. Shen, H. Wang. UBoost: Boosting with the Universum. IEEE Transactions on Pattern Analysis and Machine Intelligence. volume: 34, number: 4, pages: 825--832. 2012.

    It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name UBoost. UBoost is a boosting implementation of Vapnik’s alternative capacity concept to the large margin approach. In addition to the standard regularization term, UBoost also controls the learned model’s capacity by maximizing the number of observed contradictions. Our experiments demonstrate that UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone.
 @article{UBoost2011Shen,
   author    = "C. Shen and  P. Wang and  F. Shen and  H. Wang",
   title     = "{UBoost}: {B}oosting with the {U}niversum",
   journal   = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
   volume    = "34",
   number    = "4",
   pages     = "825--832",
   year      = "2012",
 }

Fast training of effective multi-class boosting using coordinate descent optimization

G. Lin, C. Shen, A. van den Hengel, D. Suter

Citation:
G. Lin, C. Shen, A. van den Hengel, D. Suter. Fast training of effective multi-class boosting using coordinate descent optimization. Asian Conference on Computer Vision (ACCV'12). volume: 7725, pages: 782--795. 2012.

 @inproceedings{ACCV12,
   author    = "G. Lin and  C. Shen and  A. {van den Hengel} and  D. Suter",
   title     = "Fast training of effective multi-class boosting using coordinate descent optimization",
   booktitle = "Asian Conference on Computer Vision (ACCV'12)",
   volume    = "7725",
   pages     = "782--795",
   year      = "2012",
 }

Non-sparse linear representations for visual tracking with online reservoir metric learning

X. Li, C. Shen, Q. Shi, A. Dick, A. van den Hengel

Citation:
X. Li, C. Shen, Q. Shi, A. Dick, A. van den Hengel. Non-sparse linear representations for visual tracking with online reservoir metric learning. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'12). pages: 1760--1767. 2012.

 @inproceedings{CVPR12a,
   author    = "X. Li and  C. Shen and  Q. Shi and  A. Dick and  A. {van den Hengel}",
   title     = "Non-sparse linear representations for visual tracking with online reservoir metric learning",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR'12)",
   pages     = "1760--1767",
   year      = "2012",
 }

Sharing features in multi-class boosting via group sparsity

S. Paisitkriangkrai, C. Shen, A. van den Hengel

Citation:
S. Paisitkriangkrai, C. Shen, A. van den Hengel. Sharing features in multi-class boosting via group sparsity. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'12). pages: 2128--2135. 2012.

 @inproceedings{CVPR12b,
   author    = "S. Paisitkriangkrai and  C. Shen and  A. {van den Hengel}",
   title     = "Sharing features in multi-class boosting via group sparsity",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR'12)",
   pages     = "2128--2135",
   year      = "2012",
 }

Robust tracking with weighted online structured learning

R. Yao, Q. Shi, C. Shen, Y. Zhang, A. van den Hengel

Citation:
R. Yao, Q. Shi, C. Shen, Y. Zhang, A. van den Hengel. Robust tracking with weighted online structured learning. European Conference on Computer Vision (ECCV'12). volume: 7574, pages: 158--172. 2012.

 @inproceedings{ECCV12,
   author    = "R. Yao and  Q. Shi and  C. Shen and  Y. Zhang and  A. {van den Hengel}",
   title     = "Robust tracking with weighted online structured learning",
   booktitle = "European Conference on Computer Vision (ECCV'12)",
   volume    = "7574",
   pages     = "158--172",
   year      = "2012",
 }

Is margin preserved after random projection?

Q. Shi, C. Shen, R. Hill, A. van den Hengel

Citation:
Q. Shi, C. Shen, R. Hill, A. van den Hengel. Is margin preserved after random projection?. International Conference on Machine Learning (ICML'12). 2012.
[This work provides an analysis of margin distortion under random projections, the conditions under which margins are preserved, and presents bounds on the margin distortion.]

 @inproceedings{ICML12,
   author    = "Q. Shi and  C. Shen and  R. Hill and  A. van den Hengel",
   title     = "Is margin preserved after random projection?",
   booktitle = "International Conference on Machine Learning (ICML'12)",
   url       = "http://hdl.handle.net/2440/71063",
   year      = "2012",
 }

Semidefinite programming (book chapter in: encyclopedia of computer vision, springer)

C. Shen, A. van den Hengel

Citation:
C. Shen, A. van den Hengel. Semidefinite programming (book chapter in: encyclopedia of computer vision, springer). 2012.

 @book{SDP2012,
   author    = "C. Shen and  A. {van den Hengel}",
   title     = "Semidefinite programming (book chapter in: encyclopedia of computer vision, springer)",
   year      = "2012",
 }