Related references
Note: Only part of the references are listed.A weighted SVM ensemble predictor based on AdaBoost for blast furnace Ironmaking process
Shihua Luo et al.
APPLIED INTELLIGENCE (2020)
Nonparallel support vector machine with large margin distribution for pattern classification
Liming Liu et al.
PATTERN RECOGNITION (2020)
Robust capped L1-norm twin support vector machine
Chunyan Wang et al.
NEURAL NETWORKS (2019)
Identification of uncertainty and decision boundary for SVM classification training using belief function
Javad Hamidzadeh et al.
APPLIED INTELLIGENCE (2019)
L1-Norm GEPSVM Classifier Based on an Effective Iterative Algorithm for Classification
He Yan et al.
NEURAL PROCESSING LETTERS (2018)
Top-k multi-class SVM using multiple features
Caixia Yan et al.
INFORMATION SCIENCES (2018)
Applying 1-norm SVM with squared loss to gene selection for cancer classification
Li Zhang et al.
APPLIED INTELLIGENCE (2018)
Maximum margin of twin spheres machine with pinball loss for imbalanced data classification
Yitian Xu et al.
APPLIED INTELLIGENCE (2018)
Twin support vector machine: theory, algorithm and applications
Shifei Ding et al.
NEURAL COMPUTING & APPLICATIONS (2017)
Weighted linear loss twin support vector machine for large-scale classification
Yuan-Hai Shao et al.
KNOWLEDGE-BASED SYSTEMS (2015)
Robust truncated hinge loss support vector machines
Yichao Wu et al.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2007)
Twin support vector machines for pattern classification
Jayadeva et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2007)
On ψ-learning
XT Shen et al.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2003)