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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.
2016
2016
Boosting Pulse Production Through Frontline Demonstration
The productivity of pulse crops continues to be quite low due to technological gaps in adoption of pulse technologies and other… 
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
Boosting Relation Extraction with Limited Closed-World Knowledge
This paper presents a new approach to improving relation extraction based on minimally supervised learning. By adding some… 
2006
2006
A Comparative Study of Discriminative Methods for Reranking LVCSR N-Best Hypotheses in Domain Adaptation and Generalization
This paper is an empirical study on the performance of different discriminative approaches to reranking the N-best hypotheses… 
2004
2004
Boosting water productivity.
A sustainable and secure society is one that meets its water needs without destroying the ecosystems upon which it depends or the… 
2004
2004
The Most Robust Loss Function for Boosting
Boosting algorithm is understood as the gradient descent algorithm of a loss function. It is often pointed out that the typical… 
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… 
2003
2003
Fast Face Detection Using AdaBoost
Keywords: face_det ; lts5 Reference EPFL-STUDENT-86954 Record created on 2006-06-14, modified on 2017-05-10 
2002
2002
Adapting Codes and Embeddings for Polychotomies
In this paper we consider formulations of multi-class problems based on a generalized notion of a margin and using output coding… 
2002
2002
Learning to detect multi-view faces in real-time
In this paper, we present a system which learns to detect multi-view faces. The system uses a coarse-to-fine, simple-to-complex… 
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