Sparse Support Tensor Machine with Scaled Kernel Functions

R1STM: One-class Support Tensor Machine with Randomised Kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural informati...

Scaled Support Vector Machine

Characterizing sparse preconditioner performance for the support vector machine kernel

A two-class Support Vector Machine (SVM) classifier finds a hyperplane that separates two classes of data with the maximum margin. In a first learning phase, SVM involves the construction and solution of a primal-dual interiorpoint optimization problem. Each iteration of the interior-point method (IPM) requires a sparse linear system solution, which dominates the execution time of SVM learning....

Improving support vector machine classifiers by modifying kernel functions

We propose a method of modifying a kernel function to improve the performance of a support vector machine classifier. This is based on the structure of the Riemannian geometry induced by the kernel function. The idea is to enlarge the spatial resolution around the separating boundary surface, by a conformal mapping, such that the separability between classes is increased. Examples are given spe...

Ranking Support Vector Machine with Kernel Approximation

Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (Ran...