We describe a method of dealing with sets that contain missing information in case of classification task. The described method uses multi-stage scheme that induces and combines classifiers for complete parts of the original data. The principles of the proposed Missing Template Decomposition Method are presented together with general explanation of the implementation within the RSES framework. ...

In this competition, we refined a novel algorithm for classification on large scale documents with deep category structure based on a two-stage strategy known as the deep-classifier [1]. The basic idea of deep-classifier is to take advantage of the better performance of kNN relevance search on large scale categories and the higher precision of the Naive Bayes for multi-class classification. As ...

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3...

in this article, modeling and optimization of power consumption of two–stage compressed airsystem has been investigated. to do so, the two – stage compressed air cycle with intercooler offajr petroleum company was considered. this cycle includes two centrifugal compressors, ashell, and a tube intercooler. for modeling of power consumption, actual compressors isentropicefficiencies and intercool...

In this article, we propose a general framework for multi-focal image classification and authentication, the methodology being demonstrated on microscope pollen images. The framework is meant to be generic and based on a brute force-like approach aimed to be efficient not only on any kind, and any number, of pollen images (regardless of the pollen type), but also on any kind of multi-focal imag...

One of the fundamental issues in image processing and machine vision is texture, specifically texture feature extraction, classification and abnormality detection. This thesis is concerned with the analysis and classification of natural and random textures, where the building elements and the structure of texture are not clearly determinable, hence statistical and signal processing approaches a...