Stochastic approximation algorithms for support vector machines semi-supervised binary classification

Semi-Supervised Support Vector Machines

We introduce a semi-supervised support vector machine (S3yM) method. Given a training set of labeled data and a working set of unlabeled data, S3YM constructs a support vector machine using both the training and working sets. We use S3YM to solve the transduction problem using overall risk minimization (ORM) posed by Yapnik. The transduction problem is to estimate the value of a classification ...

Semi-Supervised Support Vector Machines for Unlabeled Data Classification

A concave minimization approach is proposed for classifying unlabeled data based on the following ideas: (i) A small representative percentage (5% to 10%) of the unlabeled data is chosen by a clustering algorithm and given to an expert or oracle to label. (ii) A linear support vector machine is trained using the small labeled sample while simultaneously assigning the remaining bulk of the unlab...

Stochastic Optimization Algorithms for Support Vector Machines Classification

In this paper, we consider the problem of semi-supervised binary classification by Support Vector Machines (SVM). This problem is explored as an unconstrained and non-smooth optimization task when part of the available data is unlabelled. We apply non-smooth optimization techniques to classification where the objective function considered is non-convex and nondifferentiable and so difficult to ...

Semi - supervised Learning in Support Vector Machines

In traditional supervised classification, classifiers are trained using feature/label pairs and the classifier performance is measured on unseen test data. In the current Internet age, as the amount of data produced grows exponentially, we would like to use as much of this data as possible to train classifiers in order to get better performance. This is especially true in models such as neural ...

Report for Optimization Techniques for Semi-Supervised Support Vector Machines