Automatic relevance determination for least squares support vector machine regression

By Tony Van Gestel, Johan A. K. Suykens, B. De Moor, Joos Vandewalle. 2001

40 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

How researchers see this paper

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

In Computational Mechanics

1. Tao A. Xiang
2. Shaogang Gong
3. Chen Change Loy
4. Fei Sha
5. Soravit Changpinyo
6. Wei-Lun Chao

In Computer Vision and Pattern Recognition

1. Tao A. Xiang
2. Shaogang Gong
3. Chen Change Loy
4. Xiaogang Wang
5. Luc Van Gool
6. Yi Yang
7. Vishal M. Patel
8. Dacheng Tao

In Industrial and Manufacturing Engineering

1. Chen Change Loy

Citations in the corpus

• Variational Dropout Sparsifies Deep Neural Networks (2017, methodology): “This approach was later studied on linear models like the Rel-evance Vector Machine (Tipping, 2001) and other kernel methods (Van Gestel et al., 2001).”
• Cumulative Attribute Space for Age and Crowd Density Estimation (2013, methodology): “Improved performance can also been seen by comparing CA-RR with a number of recently proposed models [1, 4, 23, 31], all of which use the same features as input and differ only in the regression model used.”
• COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation (2015, methodology): “We perform comparison against conventional methods: recent pedestrian detetor (Detector [6])1, Least Square Support Vector Regression (LSSVR [27]), Kernel Ridge Regression (KRR [4]), Random Forest Regression (RFR [22]), Gaussian Process Regression (GPR [9]), Ridge Regression (RR [11]), Cumulative…”