Semi-supervised Instance Matching Using Boosted Classifiers

2015 ◽  
pp. 388-402 ◽  
Author(s):  
Mayank Kejriwal ◽  
Daniel P. Miranker
Keyword(s):  
Instance Matching ◽  
Boosted Classifiers

Dynamic imposter based online instance matching for person search

2020 ◽  
Vol 100 ◽  
pp. 107120
Author(s):  
Ju Dai ◽  
Pingping Zhang ◽  
Huchuan Lu ◽  
Hongyu Wang
Keyword(s):  
Instance Matching ◽  
Person Search

Evaluation of instance matching tools: The experience of OAEI

2013 ◽  
Vol 21 ◽  
pp. 49-60 ◽  
Author(s):  
A. Ferrara ◽  
A. Nikolov ◽  
J. Noessner ◽  
F. Scharffe
Keyword(s):  
Instance Matching

Tensor-Based Syntactic Feature Engineering for Ontology Instance Matching

2017 ◽  
pp. 609-622
Author(s):  
Andrzej Szwabe ◽  
Paweł Misiorek ◽  
Jarosław Bąk ◽  
Michał Ciesielczyk
Keyword(s):  
Feature Engineering ◽  
Syntactic Feature ◽  
Instance Matching

Part-Based Object Detection Using Cascades of Boosted Classifiers

2010 ◽  
pp. 556-565
Author(s):  
Xiaozhen Xia ◽  
Wuyi Yang ◽  
Heping Li ◽  
Shuwu Zhang
Keyword(s):  
Object Detection ◽  
Boosted Classifiers

scholarly journals Variance reduction trends on ‘boosted’ classifiers

2004 ◽  
Vol 8 (3) ◽  
pp. 141-154
Author(s):  
Virginia Wheway
Keyword(s):  
Empirical Study ◽  
Variance Reduction ◽  
Recent Literature ◽  
Ensemble Classification ◽  
Classification Error ◽  
Ensemble Classifiers ◽  
Training Set ◽  
Key Factor ◽  
Improved Performance ◽  
Boosted Classifiers

Ensemble classification techniques such as bagging, (Breiman, 1996a), boosting (Freund & Schapire, 1997) and arcing algorithms (Breiman, 1997) have received much attention in recent literature. Such techniques have been shown to lead to reduced classification error on unseen cases. Even when the ensemble is trained well beyond zero training set error, the ensemble continues to exhibit improved classification error on unseen cases. Despite many studies and conjectures, the reasons behind this improved performance and understanding of the underlying probabilistic structures remain open and challenging problems. More recently, diagnostics such as edge and margin (Breiman, 1997; Freund & Schapire, 1997; Schapire et al., 1998) have been used to explain the improvements made when ensemble classifiers are built. This paper presents some interesting results from an empirical study performed on a set of representative datasets using the decision tree learner C4.5 (Quinlan, 1993). An exponential-like decay in the variance of the edge is observed as the number of boosting trials is increased. i.e. boosting appears to ‘homogenise’ the edge. Some initial theory is presented which indicates that a lack of correlation between the errors of individual classifiers is a key factor in this variance reduction.

SHAPE-BASED ADULT IMAGE DETECTION

2006 ◽  
Vol 06 (01) ◽  
pp. 115-124 ◽  
Author(s):  
QING-FANG ZHENG ◽  
WEI ZENG ◽  
WEI-QIANG WANG ◽  
WEN GAO
Keyword(s):  
Skin Color ◽  
Detection Method ◽  
Skin Detection ◽  
Shape Descriptors ◽  
Shape Features ◽  
Image Detection ◽  
Classification Schemes ◽  
Skin Color Detection ◽  
Color Detection ◽  
Boosted Classifiers

This paper investigates adult images detection based on the shape features of skin regions. In order to accurately detect skin regions, we propose a skin detection method using multi-Bayes classifiers in the paper. Based on skin color detection results, shape features are extracted and fed into a boosted classifier to decide whether or not the skin regions represent a nude. We evaluate adult image detection performance using different boosted classifiers and different shape descriptors. Experimental results show that classification using boosted C4.5 classifier and combination of different shape descriptors outperforms other classification schemes.

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