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Boosting (machine learning)
Known as:
Boost
, Boosting (meta-algorithm)
, Boosting methods for object categorization
Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine…
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Related topics
Related topics
41 relations
AdaBoost
Alternating decision tree
Bias–variance tradeoff
Bootstrap aggregating
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2014
Highly Cited
2014
Word Channel Based Multiscale Pedestrian Detection without Image Resizing and Using Only One Classifier
A. Costea
,
S. Nedevschi
IEEE Conference on Computer Vision and Pattern…
2014
Corpus ID: 16294447
Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for realtime applications…
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2012
2012
Shrink boost for selecting multi-LBP histogram features in object detection
Cherkeng Heng
,
Sumio Yokomitsu
,
Yuichi Matsumoto
,
Hajime Tamura
IEEE Conference on Computer Vision and Pattern…
2012
Corpus ID: 11599612
Feature selection from sparse and high dimension features using conventional greedy based boosting gives classifiers of poor…
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2011
2011
Boosted Learning of Visual Word Weighting Factors for Bag-of-Features Based Medical Image Retrieval
Jingyan Wang
,
Yongping Li
,
Y. Zhang
,
Honglan Xie
,
Chao Wang
Sixth International Conference on Image and…
2011
Corpus ID: 18805660
In this paper, we investigate the bag-of-feature based medical image retrieval methods, which represent an image as a bag of…
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2008
2008
Real-time side vehicle tracking using parts-based boosting
Wen-Chung Chang
,
Chih-Wei Cho
IEEE International Conference on Systems, Man and…
2008
Corpus ID: 22471937
This paper presents a real-time vision-based side vehicle detection system employing a parts-based boosting algorithm. Working at…
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Highly Cited
2004
Highly Cited
2004
Efficient face orientation discrimination
S. Baluja
,
M. Sahami
,
H. Rowley
International Conference on Image Processing…
2004
Corpus ID: 5251830
The paper presents efficient methods to address the problem of discriminating between live facial orientations. We present the…
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2003
2003
Texture classification of logged forests in tropical Africa using machine-learning algorithms
Jonathan Cheung-Wai Chan
,
N. Laporte
,
Ruth S. DeFries
2003
Corpus ID: 2772407
This Letter describes a procedure that incorporates textural measures in the classification of logged forests from Landsat…
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Highly Cited
2003
Highly Cited
2003
Learning cross-document structural relationships using boosting
Zhu Zhang
,
Jahna Otterbacher
,
Dragomir R. Radev
International Conference on Information and…
2003
Corpus ID: 7609831
Multi-document discoure analysis has emerged with the potential of improving various information retrieval applications. Based on…
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2003
2003
Fast Face Detection Using AdaBoost
Julien Meynet
,
Vlad Popovici
,
J. Thiran
2003
Corpus ID: 1017280
Keywords: face_det ; lts5 Reference EPFL-STUDENT-86954 Record created on 2006-06-14, modified on 2017-05-10
2002
2002
Channel assignment strategies for a high altitude platform spot-beam architecture
D. Grace
,
C. Spillard
,
J. Thornton
,
T. Tozer
IEEE International Symposium on Personal, Indoor…
2002
Corpus ID: 17172161
Channel assignment strategies for use with a high altitude platform spot beam architecture are examined. A novel power roll-off…
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1993
1993
Redressing the balance: the advantages of informal evaluation techniques for Intelligent Learning Environments
M. Twidale
1993
Corpus ID: 13894895
The paper discusses issues to be considered when evaluating an Intelligent Learning Environment. In particular it considers…
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