Submodular Optimization for Efficient Semi-supervised Support Vector Machines

Optimization Techniques for Semi-Supervised Support Vector Machines

Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite of algorithms have recently been prop...

Report for Optimization Techniques for Semi-Supervised Support Vector Machines

Transductive Support Vector Machines

In contrast to learning a general prediction rule, V. Vapnik proposed the transductive learning setting where predictions are made only at a fixed number of known test points. This allows the learning algorithm to exploit the location of the test points, making it a particular type of semi-supervised learning problem. Transductive support vector machines (TSVMs) implement the idea of transducti...

Sparse Quasi-Newton Optimization for Semi-supervised Support Vector Machines

In real-world scenarios, labeled data is often rare while unlabeled data can be obtained in huge quantities. A current research direction in machine learning is the concept of semi-supervised support vector machines. This type of binary classification approach aims at taking the additional information provided by unlabeled patterns into account to reveal more information about the structure of ...

Multi-view Laplacian Support Vector Machines

We propose a new approach, multi-view Laplacian support vector machines (SVMs), for semi-supervised learning under the multiview scenario. It integrates manifold regularization and multi-view regularization into the usual formulation of SVMs and is a natural extension of SVMs from supervised learning to multi-view semi-supervised learning. The function optimization problem in a reproducing kern...