Support Vectors Machines have become a well established tool within machine learning.
They work well in practice and have now been used across a wide range of applications …

Kernel methods have become an increasingly popular tool for machine learning tasks such
as classification, regression or novelty detection. They exhibit good generalization …

We present an algorithm for contact detection between polygonal (2-D) or polyhedral (3-D)
convex particles in the Discrete Element Method (DEM). Noting that the space occupied by a …

摘要由于图像的低层可视特征与高层语义内容之间存在巨大的语义鸿沟, 而基于内容的图像分类
和检索准确性极大依赖低层可视特征的描述, 本文提出了一种基于多超平面支持向量机的图像 …

Support vector machines (SVMs), though accurate, are not preferred in applications
requiring great classification speed, due to the number of support vectors being large. To …

Classification is one of the most active research and application areas. Since Fisher's
famous discriminant analysis a plethora of algorithms for classification have been …

In this article, we introduce the subject of support vector machines (SVMs), describing their
applications to binary and multiclass classification as well as different SVM formulations that …

This paper proposes a method of finding a discriminative linear transformation that
enhances the data's degree of conformance to the compactness hypothesis and its inverse …

A new method is developed to determine a set of informative and refined interface
assertions satisfied by functions that are represented by feed-forward neural networks …