Safe and Efficient Screening For Sparse Support Vector Machine
Screening is an effective technique for speeding up the training process of a sparse learning model by removing the features that are guaranteed to be inactive the process. In this paper, we present a efficient screening technique for sparse support vector machine based on variational inequality. The technique is both efficient and safe.
Code (0)
등록된 구현이 없습니다.
Tasks
Sparse LearningSimilar Papers 제목 키워드 기반
Safe Sample Screening for Support Vector Machines
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 …
Safe Sample Screening for Robust Support Vector Machine
Robust support vector machine (RSVM) has been shown to perform remarkably well to improve the generalization performance of support vector machine under the noisy environment. Unfortunately, in order to handle the non-co…
Gap Safe Screening Rules for Fast Training of Robust Support Vector Machines under Feature Noise
Robust Support Vector Machines (R-SVMs) address feature noise by adopting a worst-case robust formulation that explicitly incorporates uncertainty sets into training. While this robustness improves reliability, it also l…
A Safe Screening Rule with Bi-level Optimization of $ν$ Support Vector Machine
Support vector machine (SVM) has achieved many successes in machine learning, especially for a small sample problem. As a famous extension of the traditional SVM, the $\nu$ support vector machine ($\nu$-SVM) has shown ou…
Scaling SVM and Least Absolute Deviations via Exact Data Reduction
The support vector machine (SVM) is a widely used method for classification. Although many efforts have been devoted to develop efficient solvers, it remains challenging to apply SVM to large-scale problems. A nice prope…