Efficient SVM Regression Training with SMO

By Gary William Flake, Steve Lawrence. Machine-mediated learning, 2002

329 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1fd2493d1ed77e0e9f29aae20e9353fec8aefadc

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. Ronan Collobert
2. Yoshua Bengio
3. Mohit Bansal
4. Christopher D. Manning
5. Chih-Jen Lin
6. Li Fei-Fei
7. Ruslan Salakhutdinov
8. Quoc V. Le

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Trevor Darrell
3. Xiaogang Wang
4. Ming-Hsuan Yang
5. Jitendra Malik
6. Andrew Zisserman
7. Bernt Schiele
8. Dacheng Tao

Citations in the corpus

• Kernel-Based Reinforcement Learning (2006, unknown): “Recently, some methods to deal with such issues are proposed in literature [9], [10], [11] and [12].”
• Accurate and interpretable regression trees using oracle coaching (2014, methodology): “Origin abalone 4177 8 UCI kin8fm 8192 8 Delve anacalt 566 7 KEEL kin8nh 8192 8 Delve bank8fh 8192 8 Delve kin8nm 8192 8 Delve bank8fm 8192 8 Delve laser 993 4 KEEL bank8nh 8192 8 Delve mg 1385 6 [21] bank8nm 8192 8 Delve mortage 1048 15 KEEL boston 506 13 UCI plastic 1055 2 KEEL comp 8192 12 Delve p”
• Training v-Support Vector Regression: Theory and Algorithms (2002, methodology): “Problem mg is a Mackey-Glass time series where we use the same settings as the experiments in (Flake and Lawrence 2002).”
• Large Scale Machine Learning (2004, methodology): “such as the algorithm proposed by Smola and Schölkopf (1998) and the one proposed by Flake and Lawrence (2002). We will discuss this in Chapter 5.”