相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition
Maoguo Gong et al.
NEURAL NETWORKS (2020)
Adaptive Semi-Supervised Classifier Ensemble for High Dimensional Data Classification
Zhiwen Yu et al.
IEEE TRANSACTIONS ON CYBERNETICS (2019)
Hybrid Incremental Ensemble Learning for Noisy Real-World Data Classification
Zhiwen Yu et al.
IEEE TRANSACTIONS ON CYBERNETICS (2019)
Maximizing diversity by transformed ensemble learning
Shasha Mao et al.
APPLIED SOFT COMPUTING (2019)
Differentially Private Location Privacy Preservation in Wireless Sensor Networks
Bodhi Chakraborty et al.
WIRELESS PERSONAL COMMUNICATIONS (2019)
Differential Privacy for Data and Model Publishing of Medical Data
Zongkun Sun et al.
IEEE ACCESS (2019)
Differentially private classification with decision tree ensemble
Xiaoqian Liu et al.
APPLIED SOFT COMPUTING (2018)
Margin & diversity based ordering ensemble pruning
Huaping Guo et al.
NEUROCOMPUTING (2018)
Collaborative ensemble learning under differential privacy
Tao Xiang et al.
WEB INTELLIGENCE (2018)
Differentially private random decision forests using smooth sensitivity
Sam Fletcher et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
Adaptive Ensemble Learning With Confidence Bounds
Cem Tekin et al.
IEEE TRANSACTIONS ON SIGNAL PROCESSING (2017)
Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs
Jun Hu et al.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2017)
PSPEL: In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning
Jian-Qiang Li et al.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2017)
Dynamic ensembles of exemplar-SVMs for still-to-video face recognition
Saman Bashbaghi et al.
PATTERN RECOGNITION (2017)
Considering diversity and accuracy simultaneously for ensemble pruning
Qun Dai et al.
APPLIED SOFT COMPUTING (2017)
Recognition Using Hybrid Classifiers
Margarita Osadchy et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2016)
A distributed ensemble approach for mining healthcare data under privacy constraints
Yan Li et al.
INFORMATION SCIENCES (2016)
Progressive subspace ensemble learning
Zhiwen Yu et al.
PATTERN RECOGNITION (2016)
Resampling-Based Ensemble Methods for Online Class Imbalance Learning
Shuo Wang et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2015)
On Reducing the Effect of Covariate Factors in Gait Recognition: A Classifier Ensemble Method
Yu Guan et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2015)
Hybrid Adaptive Classifier Ensemble
Zhiwen Yu et al.
IEEE TRANSACTIONS ON CYBERNETICS (2015)
Large Iterative Multitier Ensemble Classifiers for Security of Big Data
Jemal H. Abawajy et al.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING (2014)
Accuracy-diversity based pruning of classifier ensembles
Vasudha Bhatnagar et al.
PROGRESS IN ARTIFICIAL INTELLIGENCE (2014)
Local discriminative distance metrics ensemble learning
Yang Mu et al.
PATTERN RECOGNITION (2013)
Margin-based ordered aggregation for ensemble pruning
Li Guo et al.
PATTERN RECOGNITION LETTERS (2013)
Greedy optimization classifiers ensemble based on diversity
Shasha Mao et al.
PATTERN RECOGNITION (2011)
Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis
Frank McSherry
COMMUNICATIONS OF THE ACM (2010)
Hierarchical Ensemble of Global and Local Classifiers for Face Recognition
Yu Su et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2009)
Predictive Ensemble Pruning by Expectation Propagation
Huanhuan Chen et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2009)
Constructing Ensembles of Classifiers by Means of Weighted Instance Selection
Nicolas Garcia-Pedrajas
IEEE TRANSACTIONS ON NEURAL NETWORKS (2009)
Privacy-preserving boosting
Sebastien Gambs et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2007)
Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
LI Kuncheva et al.
MACHINE LEARNING (2003)
Ensembling neural networks: Many could be better than all
ZH Zhou et al.
ARTIFICIAL INTELLIGENCE (2002)
分享论文
