Intuitionistic fuzzy least square twin support vector machines for pattern classification
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Publication:6601538
DOI10.1007/S10479-022-04626-2zbMATH Open1547.6866MaRDI QIDQ6601538
Shiv Kumar Gupta, Sumit Kumar, Scindhiya Laxmi
Publication date: 10 September 2024
Published in: Annals of Operations Research (Search for Journal in Brave)
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)
Cites Work
- Title not available (Why is that?)
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- Title not available (Why is that?)
- DCA based algorithms for feature selection in multi-class support vector machine
- A direct solver with \(O(N)\) complexity for integral equations on one-dimensional domains
- Prediction of cryptocurrency returns using machine learning
- Financial market forecasting using a two-step kernel learning method for the support vector regression
- Angle-based twin support vector machine
- New support vector algorithms with parametric insensitive/margin model
- Intuitionistic fuzzy sets. Theory and applications
- Support-vector networks
- The support vector machine based on intuitionistic fuzzy number and kernel function
- Centered kernel alignment inspired fuzzy support vector machine
- An improvement on parametric \(\nu\)-support vector algorithm for classification
- Relaxed support vector regression
- Predictive modeling in a steelmaking process using optimized relevance vector regression and support vector regression
- Fuzzy Twin Support Vector Machines for Pattern Classification
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