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Do Minimal Complexity Least Squares Support Vector Machines Work?

Artificial Neural Networks in Pattern Recognition, 2023, p.53-64

2023

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Minimal Complexity Support Vector Machines for Pattern Classification

Computers (Basel), 2020-12, Vol.9 (4), p.88

Abe, Shigeo

2020

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Unconstrained large margin distribution machines

Pattern recognition letters, 2017-10, Vol.98, p.96-102

Abe, Shigeo

2017

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Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines

Artificial Neural Networks in Pattern Recognition, 2018, p.41-53

2018

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Fast Support Vector Training by Newton’s Method

Artificial Neural Networks and Machine Learning – ICANN 2011, p.143-150

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Fusing sequential minimal optimization and Newton’s method for support vector training

International journal of machine learning and cybernetics, 2016-06, Vol.7 (3), p.345-364

Abe, Shigeo

2016

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Psychological ability of representative and candidate athletes for the Tokyo 2020 (+1) Olympic games

Taiikugaku kenkyu (Japan Journal of Physical Education, Health and Sport Sciences), 2024, Vol.69, pp.317-327

2024

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Fuzzy least squares support vector machines for multiclass problems

Neural networks, 2003-06, Vol.16 (5), p.785-792

2003

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A Strategy for Supraclavicular Lymph Node Dissection Using Recurrent Laryngeal Nerve Lymph Node Status in Thoracic Esophageal Squamous Cell Carcinoma

The Annals of thoracic surgery, 2013-06, Vol.95 (6), p.1930-1937

2013

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Feature Selection by Iterative Block Addition and Block Deletion

2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013, p.2677-2682

Abe, Shigeo

2013

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Improving Generalization Abilities of Maximal Average Margin Classifiers

Artificial Neural Networks in Pattern Recognition, p.29-41

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Comparison of short-term outcomes between prone and lateral decubitus positions for thoracoscopic esophagectomy

Surgical endoscopy, 2015-09, Vol.29 (9), p.2756-2762

2015

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Fusing sequential minimal optimization and Newton’s method for support vector training

International journal of machine learning and cybernetics, 2016-06, Vol.7 (3), p.345-364

Abe, Shigeo

2016

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Soft Upper-bound Minimal Complexity LP SVMs

2021 International Joint Conference on Neural Networks (IJCNN), 2021, p.1-7

Abe, Shigeo

2021

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Incremental learning of feature space and classifier for face recognition

Neural networks, 2005-07, Vol.18 (5), p.575-584

2005

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Sparse least squares support vector training in the reduced empirical feature space

Pattern analysis and applications : PAA, 2007-08, Vol.10 (3), p.203-214

ABE, Shigeo

2007

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Batch Support Vector Training Based on Exact Incremental Training

Artificial Neural Networks - ICANN 2008, 2008, p.295-304

2008

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Fuzzy support vector machines for multilabel classification

Pattern recognition, 2015-06, Vol.48 (6), p.2110-2117

Abe, Shigeo

2015

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Support Vector Machines for Pattern Classification

Abe, Shigeo

2. Aufl., 2010

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Feature Selection by Block Addition and Block Deletion

Artificial Neural Networks in Pattern Recognition, p.48-59

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Minimal Complexity Support Vector Machines

Artificial Neural Networks in Pattern Recognition, p.89-101

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Comparison between error correcting output codes and fuzzy support vector machines

Pattern recognition letters, 2005-09, Vol.26 (12), p.1937-1945

2005

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Support Vector Machines for Pattern Classification

Abe, Shigeo

1. Aufl., 2005

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Optimizing working sets for training support vector regressors by Newton's method

2015 International Joint Conference on Neural Networks (IJCNN), 2015, p.1-8

Abe, Shigeo

2015

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Are twin hyperplanes necessary?

Pattern recognition letters, 2018-12, Vol.116, p.218-224

Abe, Shigeo

2018

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Do Minimal Complexity Least Squares Support Vector Machines Work?

Minimal Complexity Support Vector Machines for Pattern Classification

Unconstrained large margin distribution machines

Effect of Equality Constraints to Unconstrained Large Margin Distribution Machines

Fast Support Vector Training by Newton’s Method

Fusing sequential minimal optimization and Newton’s method for support vector training

Psychological ability of representative and candidate athletes for the Tokyo 2020 (+1) Olympic games

Fuzzy least squares support vector machines for multiclass problems

A Strategy for Supraclavicular Lymph Node Dissection Using Recurrent Laryngeal Nerve Lymph Node Status in Thoracic Esophageal Squamous Cell Carcinoma

Feature Selection by Iterative Block Addition and Block Deletion

Improving Generalization Abilities of Maximal Average Margin Classifiers

Comparison of short-term outcomes between prone and lateral decubitus positions for thoracoscopic esophagectomy

Fusing sequential minimal optimization and Newton’s method for support vector training

Soft Upper-bound Minimal Complexity LP SVMs

Incremental learning of feature space and classifier for face recognition

Sparse least squares support vector training in the reduced empirical feature space

Batch Support Vector Training Based on Exact Incremental Training

Fuzzy support vector machines for multilabel classification

Support Vector Machines for Pattern Classification

Feature Selection by Block Addition and Block Deletion

Minimal Complexity Support Vector Machines

Comparison between error correcting output codes and fuzzy support vector machines

Support Vector Machines for Pattern Classification

Optimizing working sets for training support vector regressors by Newton's method

Are twin hyperplanes necessary?

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