Multicategory Proximal Support Vector Machine Classifiers

By glenn. fung, Olvi L. Mangasarian. Machine-mediated learning, 2005

339 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/862529117e61569bc3fd7ec06f021a611dca8fe6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Emilio Carrizosa
2. Dolores Romero Morales
3. Feiping Nie
4. Yi Yang
5. Qi Li
6. Yueting Zhuang
7. Ying-jie Tian
8. Jieping Ye

In Computer Vision and Pattern Recognition

1. Feiping Nie
2. Yi Yang
3. Dacheng Tao
4. Yueting Zhuang
5. Shiming Xiang
6. Emilio Carrizosa
7. Dolores Romero Morales
8. Jieping Ye

In Media Technology

1. Feiping Nie
2. Qi Li
3. Yueting Zhuang
4. Ying-jie Tian
5. Jieping Ye
6. Mitsunori Ogihara
7. Shiming Xiang
8. Siep Weiland

Citations in the corpus

• Least squares linear discriminant analysis (2007, methodology): “One of the directions for future work is to compare this new formulation with other related algorithms for multi-class classiflcation algorithms [ 10 , 11, 27].”
• Local and Global Regressive Mapping for Manifold Learning with Out-of-Sample Extrapolation (2010, background): “The least squares loss function gains comparable performance to other complicated loss functions, provided that appropriate regularization is added (Fung & Mangasarian 2005).”
• A comparative study on content-based music genre classification (2003, methodology): “For multi-class objective functions, our implementation was based on MPSVM [12].”
• A Bayesian approach to identification of hybrid systems (2004, methodology): “New and efficient algorithms for data classification that allow sequential data processing started appearing recently [27], [ 28 ].”
• Supervised classification and mathematical optimization (2013, methodology): “Among others, it is worthwhile mentioning the so-called Least Squares SVM proposed in [104, 221] and the Proximal Support Vector Machine in [98, 101].”
• Learning from Imbalanced Data (2009, neutral): “vector machine (PSVM) with Newton refinement [91].”
• Robust twin support vector machine for pattern classification (2013, methodology): “1 Although literature [40] uses the Sherman–Morison–Woodbury formula ] to simply the matrix inversion’s computational complexity, it is still a cult task when the dimension or sizes of data is very high.”