Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications. By using the information of a small annotated dataset, semi-supervised clustering can produce clusters that are customized to the application domain. In this paper, we present a semi-supervised clustering technique based on a multi-objective evolutionary algorithm (NSGA-II...

Model selection is linked to model assessment, which is the problem of comparing different models, or model parameters, for a specific learning task. For supervised learning, the standard practical technique is crossvalidation, which is not applicable for semi-supervised and unsupervised settings. In this paper, a new model assessment scheme is introduced which is based on a notion of stability...

Model selection (picking, for example, a parametric model family, a prior, and an estimation criterion) is crucial for building high-accuracy classifiers. In supervised learning settings, the accuracy of a model can be estimated on a labeled set and used to guide modeling decisions. In unsupervised or type-supervised learning settings, unsupervised model selection criteria are used, but their p...

Voice activity detection, i.e., discrimination of the speech/nonspeech segments in a speech signal, is an important enabling technology for a variety of speech-based applications including the speaker recognition. In this work we provide a performance evaluation of the following supervised and unsupervised VAD algorithms in the context of text-dependent speaker recognition on the RSR2015 (Robus...

We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, that is the surface forms of morphemes. We extend a recent segmentation approach based on conditional random fields from purely supervised to semi-supervised learning by exploiting available unsupervised segmentation techniques. We integrate the unsupervised techniques into the conditional random f...

With the rapid increase of compound databases available in medicinal and material science, there is a growing need for learning representations of molecules in a semi-supervised manner. In this paper, we propose an unsupervised hierarchical feature extraction algorithm for molecules (or more generally, graph-structured objects with fixed number of types of nodes and edges), which is applicable ...