F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation
نویسندگان
چکیده
منابع مشابه
F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation
The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius. Several approaches have been proposed to integrate radius and margin for joint learning of feature transformation and SVM classifier. However, most of them either require the form of...
متن کاملLagrangian relaxation for SVM feature selection
We discuss a Lagrangian-relaxation-based heuristics for dealing with feature selection in a standard L1 norm Support Vector Machine (SVM) framework for binary classification. The feature selection model we adopt is a Mixed Binary Linear Programming problem and it is suitable for a Lagrangian relaxation approach. Based on a property of the optimal multiplier setting, we apply a consolidated nons...
متن کاملImplicit Feature Detection via a Constrained Topic Model and SVM
Implicit feature detection, also known as implicit feature identification, is an essential aspect of feature-specific opinion mining but previous works have often ignored it. We think, based on the explicit sentences, several Support Vector Machine (SVM) classifiers can be established to do this task. Nevertheless, we believe it is possible to do better by using a constrained topic model instea...
متن کاملFast Incremental SVM Learning Algorithm based on Center Convex Vector
A fast SVM learning algorithm is proposed according to incremental learning and center convex hull operator. It is established on analyzing the relevance of support vector and convex hull from the angle of calculation geometry. The convex hull of current training samples is solved in the first place. Further, Euclidean distance elimination is applied to convex hull. Meanwhile, every time when t...
متن کاملNeighborhood based sample and feature selection for SVM classification learning
Support vector machines (SVMs) are a class of popular classification algorithms for their high generalization ability. However, it is time-consuming to train SVMs with a large set of learning samples. Improving learning efficiency is one of most important research tasks on SVMs. It is known that although there are many candidate training samples in some learning tasks, only the samples near pla...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks and Learning Systems
سال: 2018
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2018.2791507