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检索条件"主题词=Boosting Algorithms"
80 条 记 录,以下是71-80 订阅
排序:
boosting automatic event extraction from the literature using domain adaptation and coreference resolution
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BIOINFORMATICS 2012年 第13期28卷 1759-1765页
作者: Miwa, Makoto Thompson, Paul Ananiadou, Sophia Univ Manchester Natl Ctr Text Min NaCTeM Manchester M1 7DN Lancs England Univ Manchester Sch Comp Sci Manchester M1 7DN Lancs England
Motivation: In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performan... 详细信息
来源: 评论
A Generic Framework for the Design of Visual-based Gesture Control Interface
A Generic Framework for the Design of Visual-based Gesture C...
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5th IEEE Conference on Industrial Electronics and Applications
作者: Chen, Wen-Hui Lin, Yu-Hong Yang, Shun-Jie Natl Taipei Univ Technol Inst Automat Technol Taipei Taiwan
Most visual-based gesture control systems are bound to specific applications. They used predefined postures for users to control devices. Users need to learn and be familiar with those predefined postures to issue a c... 详细信息
来源: 评论
Evidence contrary to the statistical view of boosting
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JOURNAL OF MACHINE LEARNING RESEARCH 2008年 第2期9卷 131-156页
作者: Mease, David Wyner, Abraham San Jose State Univ Coll Business Dept Marketing & Decis Sci San Jose CA 95192 USA Univ Penn Wharton Sch Dept Stat Philadelphia PA 19104 USA
The statistical perspective on boosting algorithms focuses on optimization, drawing parallels with maximum likelihood estimation for logistic regression. In this paper we present empirical evidence that raises questio... 详细信息
来源: 评论
Improvements of image-steganalysis using boosted combinatorial classifiers and Gaussian High Pass Filtering
Improvements of image-steganalysis using boosted combinatori...
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4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing
作者: Asadi, Nima Jamzad, Mansour Sajedi, Hedieh Sharif Univ Technol Dept Comp Engn Tehran Iran
Powerful universal steganalyzers were proposed in the literature during the past few years. In addition some studies have been conducted on improvements of current steganalysis results using information fusion techniq... 详细信息
来源: 评论
Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms
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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2004年 第3期12卷 296-308页
作者: del Jesus, MJ Hoffmann, F Navascués, LJ Sánchez, L Univ Jaen Dept Comp Sci Jaen Spain Univ Dortmund D-44227 Dortmund Germany Univ Oviedo Dept Comp Sci E-33204 Oviedo Spain
This paper proposes a novel Adaboost algorithm to learn fuzzy-rule-based classifiers. Connections between iterative learning and boosting are analyzed in terms of their respective structures and the manner these algor... 详细信息
来源: 评论
Boosted classification trees and class probability/quantile estimation
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JOURNAL OF MACHINE LEARNING RESEARCH 2007年 第3期8卷 409-439页
作者: Mease, David Wyner, Abraham J. Buja, Andreas San Jose State Univ Dept Mkt & Decis Sci San Jose CA 95192 USA Univ Penn Wharton Sch Dept Stat Philadelphia PA 19104 USA
The standard by which binary classifiers are usually judged, misclassification error, assumes equal costs of misclassifying the two classes or, equivalently, classifying at the 1/2 quantile of the conditional class pr... 详细信息
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A fast genetic method for inducting descriptive fuzzy models
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FUZZY SETS AND SYSTEMS 2004年 第1期141卷 33-46页
作者: Sánchez, L Otero, J Univ Oviedo Dept Informat Gijon 33203 Spain
Under certain inference mechanisms, fuzzy rule bases can be regarded as extended additive models. This relationship can be applied to extend some statistical techniques to learn fuzzy models from data. The interest in... 详细信息
来源: 评论
Improved boosting algorithms using confidence-rated predictions
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MACHINE LEARNING 1999年 第3期37卷 297-336页
作者: Schapire, RE Singer, Y AT&T Labs Res Shannon Lab Florham Pk NJ 07932 USA
We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analys... 详细信息
来源: 评论
BoosTexter: A boosting-based system for text categorization
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MACHINE LEARNING 2000年 第2-3期39卷 135-168页
作者: Schapire, RE Singer, Y AT&T Labs Res Shannon Lab Florham Pk NJ 07932 USA Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91904 Jerusalem Israel
This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an... 详细信息
来源: 评论
Support vector machines for spam categorization
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IEEE TRANSACTIONS ON NEURAL NETWORKS 1999年 第5期10卷 1048-1054页
作者: Drucker, H Wu, DH Vapnik, VN AT&T Bell Labs Res Red Bank NJ 07701 USA Monmouth Univ Dept Elect Engn W Long Branch NJ 07764 USA Rensselaer Polytech Inst Troy NY 12181 USA
We study the use of support vector machines (SVM's) In classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees, These four algor... 详细信息
来源: 评论