Results 31 to 40 of about 254,803 (193)
Active learning for the optimal design of multinomial classification in physics
Physical Review Research, 2022 Optimal design for model training is a critical topic in machine learning. Active learning aims at obtaining improved models by querying samples with maximum uncertainty according to the estimation model for artificially labeling; this has the additional
Yongcheng Ding +4 more
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A New Sentence-Based Interpretative Topic Modeling and Automatic Topic Labeling [PDF]
Symmetry, 2021 This article presents a new conceptual approach for the interpretative topic modeling problem. It uses sentences as basic units of analysis, instead of words or n-grams, which are commonly used in the standard approaches.The proposed approach’s specifics are using sentence probability evaluations within the text corpus and clustering of sentence ...
Olzhas Kozbagarov +2 more
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Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling
International Journal of Interactive Multimedia and Artificial Intelligence, 2019 Topic modeling algorithms are statistical methods that aim to discover the topics running through the text documents. Using topic models in machine learning and text mining is popular due to its applicability in inferring the latent topic structure of a ...
Mohsen Pourvali +2 more
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Fluorine-18: Radiochemistry and Target-Specific PET Molecular Probes Design
Frontiers in Chemistry, 2022 The positron emission tomography (PET) molecular imaging technology has gained universal value as a critical tool for assessing biological and biochemical processes in living subjects.
Yunze Wang +11 more
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International Journal of Mathematics for Industry, 2022 [Formula: see text]-labeling problem ([Formula: see text]-[Formula: see text]) is an important topic in discrete mathematics due to its various applications, like in frequency assignment in mobile communication systems, signal processing, circuit design,
Sk. Amanathulla +2 more
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A Knowledge-based Topic Modeling Approach for Automatic Topic Labeling [PDF]
International Journal of Advanced Computer Science and Applications, 2017 Probabilistic topic models, which aim to discover latent topics in text corpora define each document as a multinomial distributions over topics and each topic as a multinomial distributions over words. Although, humans can infer a proper label for each topic by looking at top representative words of the topic but, it is not applicable for machines ...
Allahyari, Mehdi +3 more
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Unsupervised Topic Labeling of Text Based on Wikipedia Categorization [PDF]
Journal of Systemics, Cybernetics and Informatics, 2019 Defining text topicality is often an expensive problem that requires significant resources for text labeling. Though many packages already exist that provide dictionaries of labeled text, synonyms, and Part-of-Speach tagging, the problem is ongoing as ...
Tetyana Loskutova
doaj
Ground Truth Data Generator for Eye Location on Infrared Driver Recordings
Journal of Imaging, 2021 Labeling is a very costly and time consuming process that aims to generate datasets for training neural networks in several functionalities and projects.
Sorin Valcan, Mihail Gaianu
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Scalable Text and Link Analysis with Mixed-Topic Link Models [PDF]
, 2013 Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content consisting of a collection of words, as well as ...
Chang J. +8 more
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Context Modeling for Ranking and Tagging Bursty Features in Text Streams [PDF]
, 2010 Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected
HE, Jing +5 more
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