Results 21 to 30 of about 9,444,467 (301)

Data Selection for Support Vector Machine Classifiers [PDF]

, 2000
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classi er, is formulated as a concave minimization problem and solved by a nite number of linear programs.
Olvi L. Mangasarian, Glenn Fung, Fung, Glenn, Olvi, Mangasarian   +3 more
core   +1 more source

The support vector decomposition machine [PDF]

Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learning performance. In previous work, many researchers have treated the learning problem in two separate phases: first use an algorithm such as singular value decomposition to ...
Francisco Pereira 0001, Geoffrey J. Gordon   +1 more
openaire   +2 more sources

Extending twin support vector machine classifier for multi-category classification problems [PDF]

, 2013
© 2013 – IOS Press and the authors. All rights reservedTwin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems.
Liu, X, Xie, J, Gao, X, Shi, Y, Hone, K, Xie, W   +5 more
core   +1 more source

A Multiclass Nonparallel Parametric-Margin Support Vector Machine

Information, 2021
The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore, this
Shu-Wang Du, Ming-Chuan Zhang, Pei Chen, Hui-Feng Sun, Wei-Jie Chen, Yuan-Hai Shao   +5 more
doaj   +1 more source

RSVM: Reduced Support Vector Machines [PDF]

, 2001
An algorithm is proposed which generates a nonlinear kernel-based separating surface that requires as little as 1% of a large dataset for its explicit evaluation.
Olvi L. Mangasarian, Mangasarian, Olvi, Yuh-Jye Lee, Lee, Yuh-Jye   +3 more
core   +1 more source

Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020–2022

Diagnostics, 2023
Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease.
Abdul Aziz K Abdul Hamid, Wan Imanul Aisyah Wan Mohamad Nawi, Muhamad Safiih Lola, Wan Azani Mustafa, Siti Madhihah Abdul Malik, Syerrina Zakaria, Elayaraja Aruchunan, Nurul Hila Zainuddin, R.U. Gobithaasan, Mohd Tajuddin Abdullah   +9 more
doaj   +1 more source

Massive Data Classification via Unconstrained Support Vector Machines [PDF]

, 2006
A highly accurate algorithm, based on support vector machines formulated as linear programs [13, 1], is proposed here as a completely unconstrained minimization problem [15].
O. L. Mangasarian, Mangasarian, Olvi, Thompson, Michael, M. E. Thompson   +3 more
core   +1 more source

BSP-Based Support Vector Regression Machine Parallel Framework [PDF]

International Journal of Networked and Distributed Computing (IJNDC), 2013
In this paper, we investigate the distributed parallel Support Vector Machine training strategy, and then propose a BSP-Based Support Vector Regression Machine Parallel Framework which can implement the most of distributed Support Vector Regression ...
Hong Zhang, Yongmei Lei
doaj   +1 more source

KLASIFIKASI PENYAKIT SIROSIS MENGGUNAKAN SUPPORT VECTOR MACHINE

E-Jurnal Matematika, 2023
Cirrhosis is one type of liver disease and is caused by forming fibrosis so that changes the liver structure become abnormal. Based on the presence of ascites, varicose veins, and bleeding, cirrhosis is divided into four clinical stages.
VANIA RISKASARI YR, I PUTU EKA NILA KENCANA, I KOMANG GDE SUKARSA   +2 more
doaj   +1 more source

Oblique Support Vector Machines

Informatica, 2005
In this paper we propose a modified framework of support vector machines, called Oblique Support Vector Machines(OSVMs), to improve the capability of classification. The principle of OSVMs is joining an orthogonal vector into weight vector in order to rotate the support hyperplanes.
Chih-Chia Yao, Pao-Ta Yu
openaire   +3 more sources