A user's guide to support vector machines.
By Asa Ben-Hur, Jason Aaron Edward Weston. 2010
871 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
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
- Maosong Sun
- Zhiyuan Liu
- Mohit Bansal
- Yoshua Bengio
- Kyunghyun Cho
- Graham Neubig
- Caglar Gulcehre
- Caiming Xiong
In Molecular Biology
- Zhiyuan Liu
- Maosong Sun
- A. Bovik
- Yoshua Bengio
- Deepti Ghadiyaram
- Cunchao Tu
- Cheng Yang
- Stefano Ermon
Citations in the corpus
- Knowledge Matters: Importance of Prior Information for Optimization (2013, background): “For the polynomial kernel, γ controls the flexibility of the classifier (degree of the polynomial) as the number of parameters increases (Hsu et al., 2003; Ben-Hur and Weston, 2010).”
- Fast Network Embedding Enhancement via High Order Proximity Approximation (2017, methodology): “We normalize each dimension of network embeddings so that the L2-norm of each dimension equals to 1 before we feed the embeddings into the classifier as suggested by [Ben-Hur and Weston, 2010].”
- Machine learning in manufacturing: advantages, challenges, and applications (2016, neutral): “…empirical results in a number of practical manufacturing applications (Chinnam, 2002; Widodo & Yang, 2007) and works very well with high-dimensional data (Azadeh et al., 2013; Ben-hur & Weston, 2010; Salahshoor et al., 2010; Sun, Rahman, Wong, & Hong, 2004; Wu, 2010; Wuest, Irgens, & Thoben, 2014).”
- Perceptual quality prediction on authentically distorted images using a bag of features approach (2016, methodology): “Given an input image (represented by a feature vector), SVM maps the high-dimensional feature vector into a visual quality score (Ben-Hur & Weston, 2010; Xu et al., 2015).”
- A data mining approach for diagnosis of coronary artery disease (2013, background): “SMO algorithm Sequential Minimal Optimization (SMO) is an algorithm for efficiently solving the optimization problem which arises during the training of Support Vector Machines (SVMs).”
- Document-level sentiment classification: An empirical comparison between SVM and ANN (2013, background): “A detailed discussion covering parameter values and their implications are beyond the scope of our work and can be found in Ben-Hur and Weston (2010), Burges (1998), Hastie et al. (2001), Haykin (1998).”