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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
AdaBoostAlternating decision treeBias–variance tradeoffBootstrap aggregating

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
Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for realtime applications… 
2013
2013
Empirical analysis of model selection criteria for genetic programming in modeling of time series system
Genetic programming (GP) and its variants have been extensively applied for modeling of the stock markets. To improve the… 
2011
2011
Boosted Learning of Visual Word Weighting Factors for Bag-of-Features Based Medical Image Retrieval
In this paper, we investigate the bag-of-feature based medical image retrieval methods, which represent an image as a bag of… 
2010
2010
Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns
Accurately detecting pedestrians in images plays a critically important role in many computer vision applications. Extraction of… 
2010
2010
A Genetic Programming Approach for Software Reliability Modeling
Genetic programming (GP) models adapt better to the reliability curve when compared with other traditional, and non-parametric… 
2008
2008
Real-time side vehicle tracking using parts-based boosting
This paper presents a real-time vision-based side vehicle detection system employing a parts-based boosting algorithm. Working at… 
Highly Cited
2004
Highly Cited
2004
Text classification by boosting weak learners based on terms and concepts
Document representations for text classification are typically based on the classical bag-of-words paradigm. This approach comes… 
2003
2003
Texture classification of logged forests in tropical Africa using machine-learning algorithms
This Letter describes a procedure that incorporates textural measures in the classification of logged forests from Landsat… 
Highly Cited
2003
Highly Cited
2003
Learning cross-document structural relationships using boosting
Multi-document discoure analysis has emerged with the potential of improving various information retrieval applications. Based on… 
1993
1993
Redressing the balance: the advantages of informal evaluation techniques for Intelligent Learning Environments
The paper discusses issues to be considered when evaluating an Intelligent Learning Environment. In particular it considers… 
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