Hierarchical Text Categorization (HTC) is the task of generating (usually by means of supervised learning algorithms) text classifiers that operate on hierarchically structured classification schemes. Notwithstanding the fact that most largesized classification schemes for text have a hierarchical structure, so far the attention of text classification researchers has mostly focused on algorithm...

In the domain of many classification problems, classes have relations of dependency that are represented in hierarchical structures. These problems are known as hierarchical classification problems. Methods based on different approaches, considering hierarchical relations in different ways, have been proposed to solve them, in the attempt to achieve better predictive performance. In this work, ...

In this paper, we suggest multiple kernel learning with hierarchical feature representations. Recently, deep learning represents excellent performance to extract hierarchical feature representations in unsupervised manner. However, since fine-tuning step of deep learning only considers global level of features for classification problems, it makes each layers hierarchical features intractable. ...

We developed a new semi-supervised EM-like algorithm that is given the set of objects present in eachtraining image, but does not know which regions correspond to which objects. We have tested thealgorithm on a dataset of 860 hand-labeled color images using only color and texture features, and theresults show that our EM variant is able to break the symmetry in the initial solution. We compared...