Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods

By Matthias W. Seeger. 2008

24 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Statistical and Nonlinear Physics

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. Jean-Philippe Vert
2. Marco Cuturi
3. Ayse Erkan
4. Yasemin Altun
5. Fabian Pedregosa
6. Mathieu Blondel
7. Quentin Berthet
8. Roy Frostig

In Computational Mathematics

1. Ayse Erkan
2. Yasemin Altun
3. Fabian Pedregosa
4. Roy Frostig

In Statistical and Nonlinear Physics

1. Ayse Erkan
2. Jean-Philippe Vert
3. Marco Cuturi
4. Yasemin Altun
5. Fabian Pedregosa
6. Mathieu Blondel
7. Stephan Hoyer
8. Alex Smola

Citations in the corpus

• Hyperparameter optimization with approximate gradient (2016, methodology): “, 1996; 1998; Bengio, 2000), has also been applied to the problem of selecting kernel parameters (Chapelle et al., 2002; Seeger, 2008) or multiple regularization parameters in log-linear models (Foo et al.”
• Semi-Supervised Learning via Generalized Maximum Entropy (2010, methodology): “Hierarchical methods proposed in (Cai & Hofmann, 2004) and (Seeger, 2008) are also instances of structured output prediction.”
• Efficient and Modular Implicit Differentiation (2021, background): “Examples of applications in machine learning include hyper-parameter optimization [23, 77, 70, 38, 12, 13], neural networks [59], and meta-learning [39, 72].”