Unsupervised learning of Swiss population spatial distribution

Deep unsupervised learning through spatial contrasting

Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks. They are, however, most suited for supervised learning from large amounts of labeled data. Previous attempts have been made to use unlabeled data to improve model performance by applying unsupervised techniques. These attempts require different architectures and training...

Spatial contrasting for deep unsupervised learning

Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks. They are, however, most suited for supervised learning from large amounts of labeled data. Previous attempts have been made to use unlabeled data to improve model performance by applying unsupervised techniques. These attempts require different architectures and training...

Spatial distribution of absorbing centers of population with seismic structure in Kerman City

Iran is located in an area with high earthquake risk. There are over 20 active faults with length of 500 km and also densely populated parts of the country have been rendered vulnerable to strong earthquakes. However, existence of eight faults of the main faults of Iran along with at least 18 faults with length of more than 100 km in Kerman province have turned the place into one of the riskies...

High-Dimensional Unsupervised Active Learning Method

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

Spatial Distribution Of Iranian Population In Canada: A Portrait Using The 2001 Census Data