Semi-supervised learning methods exploit both labeled and unlabeled data items in their training process, requiring only a small subset of labeled items. Although capable of drastically reducing the costs of labeling ...

Semi-supervised learning methods exploit both labeled and unlabeled data items in their training process, requiring only a small subset of labeled items. Although capable of drastically reducing the costs of labeling ...

Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this ...

We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity ...

In this paper, we present an automatic method, without human supervision, for the detection and classification of blue whale vocalizations from passive acoustic monitoring (PAM) data using Hidden Markov Model technology ...

Analysis of cellular phenotypes in large imaging data sets conventionally involves supervised statistical methods, which require user-annotated training data. This paper introduces an unsupervised learning method, based ...

Unsupervised land-cover classification aims at learning intrinsic properties of spectral and spatial features for the task of area coverage in urban and rural areas. In this paper, we propose to model the problem of ...