Thematische Bibliographien / Unary classifier

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Auswahl der wissenschaftlichen Literatur zum Thema „Unary classifier“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Unary classifier"

Rangkuti, Rizki Perdana, Vektor Dewanto, Aprinaldi, and Wisnu Jatmiko. "Utilizing Google Images for Training Classifiers in CRF-Based Semantic Segmentation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 3 (2016): 455–61. http://dx.doi.org/10.20965/jaciii.2016.p0455.

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One promising approach to pixel-wise semantic segmentation is based on conditional random fields (CRFs). CRF-based semantic segmentation requires ground-truth annotations to supervisedly train the classifier that generates unary potentials. However, the number of (public) annotation data for training is limitedly small. We observe that the Internet can provide relevant images for any given keywords. Our idea is to convert keyword-related images to pixel-wise annotated images, then use them as training data. In particular, we rely on saliency filters to identify the salient object (foreground)

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MONSERRAT, M., F. ROSSELLÓ, and J. TORRENS. "WHEN IS A CATEGORY OF MANY-SORTED PARTIAL ALGEBRAS CARTESIAN-CLOSED?" International Journal of Foundations of Computer Science 06, no. 01 (1995): 51–66. http://dx.doi.org/10.1142/s0129054195000056.

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In this paper we study the cartesian closedness of the five most natural categories with objects all partial many-sorted algebras of a given signature. In particular, we prove that, from these categories, only the usual one and the one having as morphisms the closed homomorphisms can be cartesian closed. In the first case, it is cartesian closed exactly when the signature contains no operation symbol, in which case such a category is a slice category of sets. In the second case, it is cartesian closed if and only if all operations are unary. In this case, we identify it as a functor category a

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Novack, T., and U. Stilla. "DISCRIMINATION OF URBAN SETTLEMENT TYPES BASED ON SPACE-BORNE SAR DATASETS AND A CONDITIONAL RANDOM FIELDS MODEL." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4 (March 11, 2015): 143–48. http://dx.doi.org/10.5194/isprsannals-ii-3-w4-143-2015.

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In this work we focused on the classification of Urban Settlement Types (USTs) based on two datasets from the TerraSAR-X satellite acquired at ascending and descending look directions. These data sets comprise the intensity, amplitude and coherence images from the ascending and descending datasets. In accordance to most official UST maps, the urban blocks of our study site were considered as the elements to be classified. The considered USTs classes in this paper are: Vegetated Areas, Single-Family Houses and Commercial and Residential Buildings. Three different groups of image attributes were

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Zhang, Bin, Cunpeng Wang, Yonglin Shen, and Yueyan Liu. "Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks." Remote Sensing 10, no. 12 (2018): 1889. http://dx.doi.org/10.3390/rs10121889.

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The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (CNN) architecture for LULC classification requires a large amount of remote sensing images. Thus, fine-tuning a pre-trained CNN for LULC detection is required. To improve the classification accuracy for high resolution remote sensing images, it is necessary to use another feature descriptor and to adopt a classifier for post-processing. A fully connected condi

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Alwaghid, Alhanoof, and Nurul Sarkar. "Exploring Malware Behavior of Webpages Using Machine Learning Technique: An Empirical Study." Electronics 9, no. 6 (2020): 1033. http://dx.doi.org/10.3390/electronics9061033.

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Malware is one of the most common security threats experienced by a user when browsing webpages. A good understanding of the features of webpages (e.g., internet protocol, port, URL, Google index, and page rank) is required to analyze and mitigate the behavior of malware in webpages. This main objective of this paper is to analyze the key features of webpages and to mitigate the behavior of malware in webpages. To this end, we conducted an empirical study to identify the features that are most vulnerable to malware attacks and its results are reported. To improve the feature selection accuracy

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Jiang, Dapeng, Jia Du, Kaishan Song, Boyu Zhao, Yiwei Zhang, and Weijian Zhang. "Classification of Conservation Tillage Using Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model." Remote Sensing 15, no. 2 (2023): 508. http://dx.doi.org/10.3390/rs15020508.

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In the remote sensing monitoring of conservation tillage, the acquisition of remote sensing data with high spatial and temporal resolution is critical. The current optical remote sensing images cannot realize both temporal and spatial resolution, especially under cloud and rain interference. Thus, this study employs the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to obtain the normalized difference tillage index (NDTI) with both temporal and spatial resolution estimated by Sentinel−2 and MODIS using the Index−then−Blend (IB) and Blend−then−Index (BI) fusion scheme

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Zhao, Ma, Zhong, Zhao, and Cao. "Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones." Remote Sensing 11, no. 23 (2019): 2828. http://dx.doi.org/10.3390/rs11232828.

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Local climate zones (LCZ) have become a generic criterion for climate analysis among global cities, as they can describe not only the urban climate but also the morphology inside the city. LCZ mapping based on the remote sensing classification method is a fundamental task, and the protocol proposed by the World Urban Database and Access Portal Tools (WUDAPT) project, which consists of random forest classification and filter-based spatial smoothing, is the most common approach. However, the classification and spatial smoothing lack a unified framework, which causes the appearance of small, isol

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Yao, W., P. Polewski, and P. Krzystek. "SEMANTIC LABELLING OF ULTRA DENSE MLS POINT CLOUDS IN URBAN ROAD CORRIDORS BASED ON FUSING CRF WITH SHAPE PRIORS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 971–76. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-971-2017.

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In this paper, a labelling method for the semantic analysis of ultra-high point density MLS data (up to 4000 points/m<sup>2</sup>) in urban road corridors is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. The CRF uses a Random Forest (RF) for generating the unary potentials of nodes and a variant of the contrastsensitive Potts model for the pair-wise potentials of node edges. The foundations of the classification are various geometric features derived by means of co-variance matrices and local

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Mishra, Bharavi, and K. K. Shukla. "Software Defect Prediction Based on GUHA Data Mining Procedure and Multi-Objective Pareto Efficient Rule Selection." International Journal of Software Science and Computational Intelligence 6, no. 2 (2014): 1–29. http://dx.doi.org/10.4018/ijssci.2014040101.

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Software defect prediction, if is effective, enables the developers to distribute their testing efforts efficiently and let them focus on defect prone modules. It would be very resource consuming to test all the modules while the defect lies in fraction of modules. Information about fault-proneness of classes and methods can be used to develop new strategies which can help mitigate the overall development cost and increase the customer satisfaction. Several machine learning strategies have been used in recent past to identify defective modules. These models are built using publicly available h

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Shu, Zhen, Kai Sun, Kaijin Qiu, and Kou Ding. "PAIRWISE-SVM FOR ON-BOARD URBAN ROAD LIDAR CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 109–13. http://dx.doi.org/10.5194/isprsarchives-xli-b1-109-2016.

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The common method of LiDAR classifications is Markov random fields (MRF). Based on construction of MRF energy function, spectral and directional features are extracted for on-board urban point clouds. The MRF energy function is consisted of unary and pairwise potentials. The unary terms are computed by SVM classifictaion. The initial labeling is mainly processed through geometrical shapes. The pairwise potential is estimated by Naïve Bayes. From training data, the probability of adjacent objects is computed by prior knowledge. The final labeling method is reweighted message-passing to minimiza

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Dissertationen zum Thema "Unary classifier"

Beneš, Jiří. "Unární klasifikátor obrazových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442432.

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The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyper parameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of reimplementation of the unary classifier.

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