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An ensemble model based on weighted support vector regression and its application in annealing heating process
2019
Science China Information Sciences
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AdaBoost [7] is an ensemble learning algorithm that can adjust the sample weights to adapt the prediction accuracy of a classifier and transforming those from weak classifiers into a strong classifier. ...
Defining a weight adjustment strategy in the regression model is the primary task in applications of the AdaBoost algorithm. ...
AdaBoost [7] is an ensemble learning algorithm that can adjust the sample weights to adapt the prediction accuracy of a classifier and transforming those from weak classifiers into a strong classifier ...
doi:10.1007/s11432-018-9673-2
fatcat:7oj57w6edjaxxakpnt7mshhoii
A Face Detection Method Based on Image Processing and Improved Adaptive Boosting Algorithm
2020
Traitement du signal
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To solve these problems, this paper puts forward an improved AdaBoost face detection method. ...
In face detection, the Adaptive Boosting (AdaBoost) algorithm consumes a long training time and faces a high false positive rate. ...
Dou and Chen [19] presented an improved AdaBoost algorithm with weighted vectors, in which each class is weighted based on its probability of being recognized by basic classifiers; the weighting process ...
doi:10.18280/ts.370306
fatcat:d7ao6a5rnfcjhhjoc747ejhfrq
Improving ADA-boost as a Popular Ensemble in Classification Problems
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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In Data Mining, several classification algorithms are used to perform classification based on single learner but classification accuracy is not in an effective manner. ...
The dependent ensemble methods like boosting and AdaBoost algorithms are promisingly provide an accurate hypothesis. Finally, AdaBoost can be a better classifier ensemble to generate accurate results. ...
The Adaboost (Adaptive Boosting) refers to popular ensemble algorithms improves boosting algorithm performance using an iterative process. It was proposed by
III. ...
doi:10.35940/ijitee.i3043.0789s319
fatcat:cssnjky4rvazjdyrx56wbhlwbu
Research on Pedestrian's Detection Based on the Integration of AdaBoost Algorithm and Shape Features
2011
Advanced Engineering Forum
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In this Paper, Propose a Pedestrian Detection Method that Based on Adaboost Algorithm and Pedestrian Shape Features Integration. ...
Candidate Region of All Pedestrians in the Image. in this Paper, Propose an Adaptive Threshold Weight Update Method, Significantly Reduced the Number of the Characteristics of Strong Classifier, Optimize ...
[1, 2] Improved AdaBoost algorithm Improved sample weights update method. ...
doi:10.4028/www.scientific.net/aef.2-3.433
fatcat:vxkhr42nlnhzrhrcjmt6vgnqc4
Multiclass Boosting with Adaptive Group-BasedkNN and Its Application in Text Categorization
2012
Mathematical Problems in Engineering
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To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group-basedkNN boosting algorithm (AGkNN-AdaBoost ...
AdaBoost is an excellent committee-based tool for classification. ...
Acknowledgments The material presented in this paper is partly based upon work supported by the China Association for Science and Technology. Experimental data is offered by the Sogou Labs. ...
doi:10.1155/2012/793490
fatcat:uyzxteeey5gd7nubhxady5zspq
Self-adaptive asymmetric on-line boosting for detecting anatomical structures
2012
Medical Imaging 2012: Computer-Aided Diagnosis
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Benefits and Advantages • The asymmetric loss function is self-adjusting during on-line training • Updates sample's importance weight based on classification result and sample's label • Validated from ...
There are a variety of available and popular boosting algorithms including AdaBoost, TotalBoost, MadaBoost etc., with subtle differences in their weighting of training data and hypotheses. ...
There are a variety of available and popular boosting algorithms including AdaBoost, TotalBoost, MadaBoost etc., with subtle differences in their weighting of training data and hypotheses. ...
doi:10.1117/12.912551
dblp:conf/micad/WuTXL12
fatcat:6y4cjj3jqbeqzmeyvenxph2cma
A Boosted Adaptive Particle Filter for Face Detection and Tracking
2006
2006 International Conference on Image Processing
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The proposed BAPF scheme consists of an AdaBoost face detection model which performs multiview face detection using a trained AdaBoost algorithm, and an APF face tracking model based on visual contour ...
The value of γ is typically adjusted based on varying scene conditions determined by clutter, illumination and occlusions. ...
Eq. (13) is termed as the Adaptive Learning Constraint (ALC) whose parameters l K and α can be learned during the iterative computation. ...
doi:10.1109/icip.2006.312995
dblp:conf/icip/ZhengB06
fatcat:egnhrifimvbghnpu4lhdtzzogq
A Comparison of Model Aggregation Methods for Regression
[chapter]
2003
Lecture Notes in Computer Science
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Our experiments reveal that different types of AdaBoost algorithms require different complexities of base models. ...
In this paper we examine their adaptation to regression, and benchmark them on synthetic and real-world data. ...
Distribution-Based Algorithms Drucker's AdaBoost. Drucker's AdaBoost for regression [5] is an ad hoc adaption of the classification AdaBoost. ...
doi:10.1007/3-540-44989-2_10
fatcat:kw72hx42jveonmxmdsvczbojmq
AdaBoost for Feature Selection, Classification and Its Relation with SVM, A Review
2012
Physics Procedia
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A sort of cascaded support vector machines architecture is capable of improving the classification accuracy based on AdaBoost boosting algorithm, namely, AdaboostSVM. ...
The original adaptive boosting algorithm and its application in face detection and facial expression recognition are reviewed. ...
Adaboost Algorithm AdaBoost algorithm creates a set of poor learners by maintaining a collection of weights over training data and adjusts them after each weak learning cycle adaptively. ...
doi:10.1016/j.phpro.2012.03.160
fatcat:5lomkwoeavf7pc7bechcxg3qeu
A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm
2020
Traitement du signal
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After that, a strong AdaBoost classifier was designed based on optimized weak BPNN classifiers, and the weight distribution strategy of AdaBoost was further improved. ...
The improved PSO was adopted to optimize the initial connection weights and output thresholds of the neural network. ...
[18] developed a fast pedestrian detection algorithm based on auto-encoding neural network and AdaBoost: the images were processed by the pedestrian detection algorithm based on aggregated channel feature ...
doi:10.18280/ts.370606
fatcat:3c4dktxztbd4hdcpf2rrwili4y
Parameter Inference of Cost-Sensitive Boosting Algorithms
[chapter]
2005
Lecture Notes in Computer Science
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Then, their identification abilities on the small classes are tested on four "real world" medical data sets taken from UCI Machine Learning Database based on F-measure. ...
In this paper, we come up with three versions of cost-sensitive AdaBoost algorithms where the parameters for sample weight updating are induced. ...
AdaBoost (Adaptive Boosting) algorithm, introduced by Freund and Schapire [7, 12] , is reported as an effective boosting algorithm to improve classification accuracy. ...
doi:10.1007/11510888_3
fatcat:z4gethpek5djlmmbhijda6cxku
iBoost: Boosting Using an instance-Based Exponential Weighting Scheme
[chapter]
2002
Lecture Notes in Computer Science
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Also, Helmbold, Kwek and Pitt that showed in the prediction using a pool of experts framework an instance based weighting scheme improves performance. ...
Motivated by these results, we propose here an instance-based exponential weighting scheme in which the weights of the base classifiers are adjusted according to the test instance x. ...
Out of curiosity, we decided to investigate whether adapting an exponential weighting scheme in AdaBoost improves prediction accuracy. Unfortunately, we found a decrease in prediction accuracy. ...
doi:10.1007/3-540-36755-1_21
fatcat:lcyg4i7linfh7dryat7ypv3bxa
A modified real AdaBoost algorithm to discover intensive care unit subgroups with a poor outcome
2013
AMIA Annual Symposium Proceedings
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A new method based on adaptive decision tree boosting discovered many subgroups of ICU patients for which there is potentially room for outcomes improvement. ...
We investigate whether poor outcomes of an ICU can be traced back to excess deaths in specific patient subgroups, by discovering candidate subgroups, with a modified adaptive decision tree boosting algorithm ...
Modified Real AdaBoost algorithm Adaptive boosting 3 is an ensemble-based machine learning method in which different base models are constructed on the same dataset in a series of R 'rounds'. ...
pmid:24551376
pmcid:PMC3900217
fatcat:s6vhecckbjaghfin24nwqlow7u
Optimization of intelligent heating ventilation air conditioning system in urban building based on BIM and artificial intelligence technology
2021
Computer Science and Information Systems
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A prediction model of heating ventilation air conditioning (HVAC) energy consumption is established by using back propagation neural network (BPNN) and adapted boosting (Adaboost) algorithm. ...
Finally, the effectiveness of the urban intelligent HVAC optimization prediction model based on BIM and artificial intelligence (AI) is further verified by simulation experiments. ...
Based on the obtained error rate, the weight of the sample in the training data is adjusted to reduce the weight value of the accurate sample data of each classification, and the weight value of the wrong ...
doi:10.2298/csis200901027l
fatcat:x4htzaxomrawjcd44l4wrnf24u
Crop region extraction of remote sensing images based on fuzzy ARTMAP and adaptive boost
2015
Journal of Intelligent & Fuzzy Systems
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With more samples, the algorithm efficiency is greatly affected. This paper proposes an improved fuzzy ARTMAP (FAM) with an adaptive boost strategy, namely Adaboost FAM. ...
This paper introduces texture information based on a Gabor filter group to enrich land-cover information and establish a spectrum-texture feature set. ...
The work of this paper uses the adaptive boost strategy to optimize FAM classifiers, constructs weak classifier group which is based on less samples and simple feature set, gets Adaboost FAM classi-fier ...
doi:10.3233/ifs-151983
fatcat:3gvn5tpj35clteneulyaz46yaq
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