Optimization Techniques for Semi-Supervised Support Vector Machines

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 ...

Branch and Bound for Semi-Supervised Support Vector Machines

Semi-supervised SVMs (SVM) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The associated optimization problem is non-convex. To examine the full potential of SVMs modulo local minima problems in current implementations, we apply branch and bound techniques for obtaining exact, globally optimal solutions. Empirical evidence suggests that the...

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...