Continuous Hyper-parameter Learning for Support Vector Machines
By Teresa Klatzer, Thomas Pock. 2015
25 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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
- Stephen Gould
- Basura Fernando
- Anoop Cherian
- Peter Anderson
- Joe Naoum-Sawaya
- Richard I. Hartley
- Dylan Campbell
- Simon Robert Arridge
In Computer Vision and Pattern Recognition
- Stephen Gould
- Luc Van Gool
- Dacheng Tao
- Trevor Darrell
- Xiaogang Wang
- Basura Fernando
- Cordelia Schmid
- Andrew Zisserman
Citations in the corpus
- On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization (2016, methodology): “…in machine learning and computer vision where they have been applied to parameter and hyper-parameter learning [Do et al., 2007, Domke, 2012, Klatzer and Pock, 2015], image denoising [Samuel and Tappen, 2009, Ochs et al., 2015], and most recently, video activity recognition [Fernando and…”
- Deep Declarative Networks (2019, background): “Last, Klatzer and Pock [36] propose a bi-level optimization framework for choosing hyper-parameter settings for support vector machines that avoids the need for cross-validation.”
- Learning End-to-end Video Classification with Rank-Pooling (2016, background): “It has recently gained interest in the machine learning community in the context of hyperparameter learning (Klatzer & Pock, 2015; Do et al., 2007) and in the computer vision community in the context of image denoising (Domke, 2012; Kunisch & Pock, 2013).”
- Bilevel approaches for learning of variational imaging models (2015, background): “Interesting recent works also include bilevel learning approaches for image segmentation [67] and learning of support vector machines [51].”
- Optimization Models for Machine Learning: A Survey (2019, background): “Alternatively, HPO can be modeled as a box-constrained mathematical optimization problem [82], or as a bilevel optimization problem as discussed in [96, 140, 166], which provides theoretical convergence guarantees in addition to computational advantage.”
- Solving inverse problems using data-driven models (2019, background): “learning an optimal set-up of support vector machines (Klatzer and Pock 2015)).”