ssSVMToolbox / News: Recent posts

In the download section you can find the bachelor thesis 'Semi-Supervised Learning With Support Vector Machines' and binaries for different OS.

Semi-supervised learning is a combination of supervised and unsupervised learning where typically a small amount of labeled and a large amount of unlabeled data are used for training. This is done because of two reasons. First labeling of a huge set of instances can be a time-consuming task. This classification has to be done by a skilled human expert and can be quiet costly. Semi-supervised learning reduces the needed amount of labeled instances and the associated costs. Note that in contrast the acquisition of the unlabeled data is usually relatively inexpensive. Second it has been shown that using unlabeled data for learning improves the accuracy of the produced learner. Summing up the advantages of semi-supervised learning are (in many cases) better accuracy, fewer data and less training time. To achieve these the examples to be labeled should be selected properly.... read more

One result of my bachelor thesis 'Semi-supervised Learning With Support Vector Machines' is the ssSVMToolbox. It is a plattform to conduct supervised and semi-supervised datamining experiments. It is based on Spring, Eclipse RCP and RapidMiner.