4.6 Article

EEG signal classification using improved intuitionistic fuzzy twin support vector machines

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。
Article Computer Science, Artificial Intelligence

Large-scale pinball twin support vector machines

M. Tanveer et al.

Summary: This study proposes a novel large scale pinball twin support vector machine (LPTWSVM) to address the limitations of the twin support vector machines (TWSVMs), using a unique pinball loss function and improving model performance by eliminating matrix inversion calculation and minimizing structural risk.

MACHINE LEARNING (2022)

Article Multidisciplinary Sciences

A Novel Twin Support Vector Machine with Generalized Pinball Loss Function for Pattern Classification

Wanida Panup et al.

Summary: This study introduces a novel support vector machine algorithm called GPin-TSVM, which is able to solve data classification problems that are less sensitive to noise and preserve sparse solutions. Experimental results demonstrate that this algorithm outperforms existing classifiers in terms of accuracy and has also been successfully applied in handwritten digit recognition applications.

SYMMETRY-BASEL (2022)

Review Operations Research & Management Science

Comprehensive review on twin support vector machines

M. Tanveer et al.

Summary: TWSVM and TSVR are emerging machine learning techniques for classification and regression challenges. TWSVM classifies data points using two nonparallel hyperplanes, while TSVR is based on TWSVM and solves two SVM-type problems. Although there has been progress in research on these techniques, there is limited literature on the comparison of different variants of TSVR.

ANNALS OF OPERATIONS RESEARCH (2022)

Article Computer Science, Artificial Intelligence

Large-Scale Fuzzy Least Squares Twin SVMs for Class Imbalance Learning

M. A. Ganaie et al.

Summary: In this article, a novel large-scale fuzzy least squares TSVM method is proposed to handle large-scale data. The method avoids the shortcomings of traditional TSVMs in large-scale data processing by using structural risk minimization principle and positive-definite matrices. Experimental results demonstrate the superior performance of the proposed method in large-scale classification problems and its effectiveness in addressing class imbalance.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2022)

Article Computer Science, Information Systems

Large-Scale Least Squares Twin SVMs

M. Tanveer et al.

Summary: The proposed LS-LSTSVM addresses the shortcomings of TWSVM and LSTSVM by introducing a different Lagrangian function to eliminate the need for calculating inverse matrices, using the kernel trick directly for non-linear cases, and minimizing structural risk. These improvements aim to enhance classification accuracy on datasets, especially for large-scale problems.

ACM TRANSACTIONS ON INTERNET TECHNOLOGY (2021)

Article Computer Science, Artificial Intelligence

Twin-parametric margin support vector machine with truncated pinball loss

Huiru Wang et al.

Summary: The paper introduces a novel classifier TPin-TSVM with noise insensitivity, sparsity, and efficient optimization, verified through experiments.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Computer Science, Artificial Intelligence

Intuitionistic Fuzzy Proximal Support Vector Machines for Pattern Classification

Scindhiya Laxmi et al.

NEURAL PROCESSING LETTERS (2020)

Article Computer Science, Artificial Intelligence

Sparse pinball twin support vector machines

M. Tanveer et al.

APPLIED SOFT COMPUTING (2019)

Article Computer Science, Information Systems

General twin support vector machine with pinball loss function

M. Tanveer et al.

INFORMATION SCIENCES (2019)

Article Computer Science, Artificial Intelligence

Comprehensive evaluation of twin SVM based classifiers on UCI datasets

M. Tanveer et al.

APPLIED SOFT COMPUTING (2019)

Article Computer Science, Artificial Intelligence

Intuitionistic Fuzzy Twin Support Vector Machines

Salim Rezvani et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2019)

Article Computer Science, Artificial Intelligence

A robust fuzzy least squares twin support vector machine for class imbalance learning

B. Richhariya et al.

APPLIED SOFT COMPUTING (2018)

Article Automation & Control Systems

Support vector machine with truncated pinball loss and its application in pattern recognition

Liming Yang et al.

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2018)

Article Computer Science, Artificial Intelligence

EEG signal classification using universum support vector machine

B. Richhariya et al.

EXPERT SYSTEMS WITH APPLICATIONS (2018)

Article Computer Science, Artificial Intelligence

Support vector machine classifier with truncated pinball loss

Xin Shen et al.

PATTERN RECOGNITION (2017)

Article Computer Science, Artificial Intelligence

Least squares recursive projection twin support vector machine for multi-class classification

Zhi-Min Yang et al.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2016)

Article Computer Science, Artificial Intelligence

Robust energy-based least squares twin support vector machines

Mohammad Tanveer et al.

APPLIED INTELLIGENCE (2016)

Article Computer Science, Artificial Intelligence

Multiple recursive projection twin support vector machine for multi-class classification

Chun-Na Li et al.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2016)

Proceedings Paper Computer Science, Cybernetics

Coordinate Descent Fuzzy Twin Support Vector Machine for Classification

Bin-Bin Gao et al.

2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) (2015)

Article Computer Science, Artificial Intelligence

Support Vector Machine Classifier with Pinball Loss

Xiaolin Huang et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2014)

Article Computer Science, Artificial Intelligence

Robust twin support vector machine for pattern classification

Zhiquan Qi et al.

PATTERN RECOGNITION (2013)

Article Computer Science, Artificial Intelligence

The support vector machine based on intuitionistic fuzzy number and kernel function

Minghu Ha et al.

SOFT COMPUTING (2013)

Article Computer Science, Artificial Intelligence

A Web Search Engine-Based Approach to Measure Semantic Similarity between Words

Danushka Bollegala et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2011)

Article Computer Science, Artificial Intelligence

Improvements on Twin Support Vector Machines

Yuan-Hai Shao et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2011)

Article Computer Science, Artificial Intelligence

EEG signal classification using PCA, ICA, LDA and support vector machines

Abdulhamit Subasi et al.

EXPERT SYSTEMS WITH APPLICATIONS (2010)

Article Computer Science, Artificial Intelligence

Least squares twin support vector machines for pattern classification

M. Arun Kumar et al.

EXPERT SYSTEMS WITH APPLICATIONS (2009)

Article Operations Research & Management Science

Optimal kernel selection in twin support vector machines

Reshma Khemchandani et al.

OPTIMIZATION LETTERS (2009)

Article Engineering, Electrical & Electronic

Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm

Hasan Ocak

SIGNAL PROCESSING (2008)

Article Computer Science, Artificial Intelligence

Twin support vector machines for pattern classification

Jayadeva et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2007)

Article Computer Science, Artificial Intelligence

Multisurface proximal support vector machine classification via generalized eigenvalues

OL Mangasarian et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2006)

Article Physics, Multidisciplinary

Quantitative EEG analysis of the maturational changes associated with childhood absence epilepsy

OA Rosso et al.

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2005)

Article Remote Sensing

Support vector machines for classification in remote sensing

M Pal et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2005)

Article Biochemical Research Methods

Analysis of EEG records in an epileptic patient using wavelet transform

H Adeli et al.

JOURNAL OF NEUROSCIENCE METHODS (2003)

Article Computer Science, Artificial Intelligence

Fuzzy support vector machines

CF Lin et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2002)