Homotopy Continuation Approaches for Robust SV Classification and Regression

no code implementations • 12 Jul 2015 • Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Masayuki Karasuyama, Ichiro Takeuchi

An advantage of our homotopy approach is that it can be interpreted as simulated annealing, a common approach for finding a good local optimal solution in non-convex optimization problems.

Classification General Classification +3

An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models

no code implementations • 10 Feb 2014 • Yoshiki Suzuki, Kohei Ogawa, Yuki Shinmura, Ichiro Takeuchi

If a reasonably good suboptimal model is available, our algorithm can compute lower and upper bounds of many useful quantities for making inferences on the unknown target model.

Model Selection

Safe Sample Screening for Support Vector Machines

no code implementations • 27 Jan 2014 • Kohei Ogawa, Yoshiki Suzuki, Shinya Suzumura, Ichiro Takeuchi

Sparse classifiers such as the support vector machines (SVM) are efficient in test-phases because the classifier is characterized only by a subset of the samples called support vectors (SVs), and the rest of the samples (non SVs) have no influence on the classification result.