Výsledky vyhľadávania - "Multi-label Text Classification in Machine Learning"

A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors

Autori: Sabri, Mohammed Verde, Rosanna Balzanella, Antonio a ďalší

Zdroj: Journal of Classification. 41:264-288

Predmety: Artificial intelligence, 02 engineering and technology, Pattern recognition (psychology), 7. Clean energy, Clustering Algorithms, Cluster analysis, Artificial Intelligence, Document Clustering, Multi-label Text Classification in Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Canopy clustering algorithm, Data mining, Data Clustering Techniques and Algorithms, Spectral Clustering, 4. Education, Correlation clustering, 15. Life on land, k-means clustering, Semi-supervised Clustering, Computer science, Nearest-neighbor chain algorithm, Algorithm, Computer Science, Physical Sciences, Computer Vision and Pattern Recognition, k-nearest neighbors algorithm, Face Recognition and Dimensionality Reduction Techniques, Density-based Clustering, Mathematics

Arabic Ontology for Hadith texts - A survey

Autori: Mariam Muhammed Shahira Shaaban Azab Najwa F.Ali a ďalší

Zdroj: The Egyptian Journal of Language Engineering. 11:1-14

Predmety: Syntax-based Translation Models, Arabic, Ontology, Natural language processing, 4. Education, Linguistics, Statistical Machine Translation and Natural Language Processing, Epistemology, Computer science, Language Modeling, FOS: Philosophy, ethics and religion, Inorganic Chemistry, Chemistry, Philosophy, Artificial Intelligence, Homogeneous Catalysis with Transition Metals, FOS: Chemical sciences, Part-of-Speech Tagging, Multi-label Text Classification in Machine Learning, Computer Science, Physical Sciences, FOS: Languages and literature, Information retrieval, Text Classification

An Interpretable Fuzzy Graph Learning for Label Propagation Assisting Data Classification

Autori: Cherukula Madhu M. Sudhakar

Zdroj: IEEE Transactions on Fuzzy Systems. 32:1331-1345

Predmety: Intuitionistic Fuzzy Sets, Artificial intelligence, Coordinate descent, Social Sciences, Multi-Criteria Decision Making, Management Science and Operations Research, Pattern recognition (psychology), Computer science, Feature vector, Graph, Decision Sciences, Urban Studies, Theoretical computer science, Fuzzy Sets, Artificial Intelligence, Outlier, Multi-label Text Classification in Machine Learning, Computer Science, Physical Sciences, Feature selection, Machine learning, Interpretability, Dynamics of Urban Structure through Spatial Network Analysis, Multi-label Learning, Data mining

Simultaneous instance pooling and bag representation selection approach for multiple-instance learning (MIL) using vision transformer

Autori: Muhammad Waqas Muhammad Atif Tahir Muhammad Danish Author a ďalší

Zdroj: Neural Computing and Applications. 36:6659-6680

Predmety: Pooling, Artificial intelligence, Shape Matching, Bag representation selection, FOS: Political science, FOS: Law, 02 engineering and technology, Pattern recognition (psychology), Quantum mechanics, 7. Clean energy, Computational Science and Engineering, Selection (genetic algorithm), Image Feature Retrieval and Recognition Techniques, Artificial Intelligence, Shape Matching and Object Recognition, Multi-label Text Classification in Machine Learning, Machine learning, Vision transformers, 0202 electrical engineering, electronic engineering, information engineering, Multi-label Learning, Political science, Object Recognition, Attention-based pooling, Transformer, Physics, 4. Education, Politics, Feature learning, Voltage, Cross-Modal Retrieval, 15. Life on land, Computer science, Multiple-instance learning (MIL), Computer Science, Physical Sciences, Feature selection, Computer Vision and Pattern Recognition, Representation (politics), Law, Feature Matching

Popis súboru: application/pdf

Prístupová URL adresa: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85185142836&origin=inward
https://hdl.handle.net/10576/60021

The one-sayers model for the Extended Crosswise design

Autori: Maarten Cruyff Khadiga H. A. Sayed Andrea Petróczi a ďalší

Zdroj: Journal of the Royal Statistical Society Series A: Statistics in Society. 187:882-899

Predmety: Response bias, Statistics and Probability, Survey Sampling, 05 social sciences, 01 natural sciences, 0504 sociology, Artificial Intelligence, Cheating, Physical Sciences, Multi-label Text Classification in Machine Learning, Computer Science, Doping, FOS: Mathematics, Self-protection, 0101 mathematics, Randomized response, Statistical Methods for Sensitive Survey Questions, Model-Based Clustering with Mixture Models, 10. No inequality, Mathematics

Quran reciter identification using NASNetLarge

Autori: Hebat-Allah Saber Ahmed Younes Mohammad Osman a ďalší

Zdroj: Neural Computing and Applications. 36:6559-6573

Predmety: Neural Machine Translation, Artificial intelligence, 4. Education, Botany, 02 engineering and technology, Statistical Machine Translation and Natural Language Processing, Computer science, Identification (biology), Machine Translation, Computational Science and Engineering, Artificial Intelligence, Multi-label Text Classification in Machine Learning, Computer Science, Physical Sciences, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Biology, Natural Language Processing

A HYBRID FEATURE SELECTION APPROACH FOR ROMAN URDU TEXT CLASSIFICATION

Autori: Waqas Azeem Aziza Chakir

Zdroj: Journal of Advancement in Computing. 2:39-44

Predmety: Artificial intelligence, Feature (linguistics), Natural language processing, 4. Education, Linguistics, Pattern recognition (psychology), 16. Peace & justice, Urdu, Computer science, FOS: Philosophy, ethics and religion, Philosophy, Selection (genetic algorithm), Artificial Intelligence, Multi-label Text Classification in Machine Learning, Computer Science, Physical Sciences, Feature selection, FOS: Languages and literature, Feature Selection, Text Classification

Unravelling Emotional Tones: A Hybrid Optimized Model for Sentiment Analysis in Tamil Regional Languages

Autori: M. Sangeetha K. Nimala

Zdroj: Journal of Machine and Computing. :114-126

Predmety: Tamil, Artificial intelligence, Ambiguity, Support vector machine, 02 engineering and technology, 01 natural sciences, 7. Clean energy, Sentiment analysis, Identification (biology), Artificial Intelligence, Aspect-based Sentiment Analysis, Global Positioning System, 0103 physical sciences, Multi-label Text Classification in Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Sentiment Analysis, Psychology, 10. No inequality, Biology, Hindi, Naive Bayes classifier, Adaptation (eye), Natural language processing, 4. Education, Botany, Ambiguity resolution, Linguistics, Computer science, FOS: Philosophy, ethics and religion, Programming language, Automatic Keyword Extraction from Textual Data, FOS: Psychology, Philosophy, Sentiment Analysis and Opinion Mining, Natural language, Emotion Recognition, GNSS applications, Computer Science, Physical Sciences, 8. Economic growth, FOS: Languages and literature, Telecommunications, Language identification, Neuroscience

Aspect Based Sentiment Analysis: Feature Extraction using Latent Dirichlet Allocation (LDA) and Term Frequency - Inverse Document Frequency (TF-IDF) in Machine Learning (ML)

Autori: Shakirah Mohd Sofi Ali Selamat

Zdroj: Malaysian Journal of Information and Communication Technology (MyJICT). :169-179

Predmety: Artificial intelligence, Text Mining, Feature (linguistics), Latent Dirichlet allocation, Geometry, Pattern recognition (psychology), Twitter Sentiment, Quantum mechanics, Term (time), tf–idf, Topic model, Artificial Intelligence, Aspect-based Sentiment Analysis, Multi-label Text Classification in Machine Learning, Sentiment Analysis, FOS: Mathematics, Natural language processing, Physics, Linguistics, Computer science, FOS: Philosophy, ethics and religion, 3. Good health, Automatic Keyword Extraction from Textual Data, Philosophy, Sentiment Analysis and Opinion Mining, Computer Science, Physical Sciences, FOS: Languages and literature, Feature extraction, Inverse, Mathematics

Domain Adaptation in Nested Named Entity Recognition From Scientific Articles in Agriculture

Autori: D. T. Phan Phuoc Vinh Linh Le Ngoc Hoang Luong a ďalší

Zdroj: Proceedings of the 12th International Symposium on Information and Communication Technology. :48-55

Predmety: Artificial intelligence, Information extraction, Economics, Oceanography, Mathematical analysis, Data science, Task (project management), Context (archaeology), Artificial Intelligence, Multi-label Text Classification in Machine Learning, FOS: Mathematics, Information retrieval, Natural Language Processing, 2. Zero hunger, Domain (mathematical analysis), Geography, Baseline (sea), Natural language processing, Geology, Statistical Machine Translation and Natural Language Processing, FOS: Earth and related environmental sciences, 15. Life on land, Named Entity Recognition, Computer science, Management, Archaeology, Categorization, Computer Science, Physical Sciences, Named-entity recognition, Mathematics

ACROSS: An Alignment-based Framework for Low-Resource Many-to-One Cross-Lingual Summarization

Autori: Peiyao Li Zhengkun Zhang Jun Wang a ďalší

Zdroj: Findings of the Association for Computational Linguistics: ACL 2023. :2458-2472

Predmety: Artificial intelligence, Economics, Resource (disambiguation), Automatic summarization, Oceanography, Data science, Task (project management), Artificial Intelligence, Multi-label Text Classification in Machine Learning, Information retrieval, Multi-label Learning, Natural Language Processing, Computer network, Topic Modeling, Baseline (sea), Natural language processing, Geology, Statistical Machine Translation and Natural Language Processing, FOS: Earth and related environmental sciences, Computer science, Language Modeling, Management, Programming language, Computer Science, Physical Sciences, Pipeline (software), Consistency (knowledge bases)

AlphaBrains at WojoodNER shared task: Arabic Named Entity Recognition by Using Character-based Context-Sensitive Word Representations

Autori: Toqeer Ehsan Amjad Ali Ala Al‐Fuqaha

Zdroj: Proceedings of ArabicNLP 2023. :783-788

Predmety: Syntax-based Translation Models, Artificial intelligence, Character (mathematics), Economics, Word (group theory), Geometry, Task (project management), Context (archaeology), Artificial Intelligence, Multi-label Text Classification in Machine Learning, FOS: Mathematics, Multi-label Learning, Biology, Natural Language Processing, Arabic, Topic Modeling, Natural language processing, Paleontology, Linguistics, Statistical Machine Translation and Natural Language Processing, Named Entity Recognition, Computer science, Language Modeling, FOS: Philosophy, ethics and religion, Management, Philosophy, Computer Science, Physical Sciences, FOS: Languages and literature, Mathematics

Prístupová URL adresa: https://cris.vtt.fi/en/publications/ddb711e4-7cb2-49b9-9c61-8db64fce7ac8

Ontology based Feature Selection and Weighting for Text classification using Machine Learning

Autori: Djelloul Bouchiha Abdelghani Bouziane Noureddine Doumi

Zdroj: Journal of Information Technology and Computing. 4:1-14

Predmety: FOS: Computer and information sciences, Artificial intelligence, Support vector machine, Class (philosophy), Feature (linguistics), 02 engineering and technology, Epistemology, Weighting, Pattern recognition (psychology), Feature vector, Web Data Extraction and Crawling Techniques, Machine Learning Algorithms, Selection (genetic algorithm), Artificial Intelligence, Multi-label Text Classification in Machine Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Syntax, Feature Selection, Multi-label Learning, Content Adaptation, Text Classification, Preprocessor, Ontology, Natural language processing, 4. Education, Linguistics, Computer science, FOS: Philosophy, ethics and religion, Automatic Keyword Extraction from Textual Data, Philosophy, Computer Science, Physical Sciences, Feature selection, FOS: Languages and literature, Medicine, Radiology, Information Systems

Sentiment analysis of sarcasm detection in social media

Autori: S.K. Alaramma Adamu Adamu Habu Brian Yau a ďalší

Zdroj: Gadau Journal of Pure and Allied Sciences. 2:76-82

Predmety: Irony, Artificial intelligence, Support vector machine, Social psychology, 01 natural sciences, Social media, Sentiment analysis, Feeling, Context (archaeology), Artificial Intelligence, Aspect-based Sentiment Analysis, Multi-label Text Classification in Machine Learning, Mood, Machine learning, 0502 economics and business, Sentiment Analysis, Psychology, Biology, 0105 earth and related environmental sciences, Sarcasm, Naive Bayes classifier, Natural language processing, 05 social sciences, Paleontology, Linguistics, 16. Peace & justice, Computer science, FOS: Philosophy, ethics and religion, Automatic Keyword Extraction from Textual Data, FOS: Psychology, World Wide Web, Philosophy, Sentiment Analysis and Opinion Mining, Emotion Recognition, Computer Science, Physical Sciences, FOS: Languages and literature

A high-quality feature selection method based on frequent and correlated items for text classification

Autori: Heba Mamdouh Farghaly Tarek Abd El‐Hafeez

Zdroj: Soft Computing. 27:11259-11274

Predmety: FOS: Computer and information sciences, Artificial intelligence, Metric (unit), Feature (linguistics), FOS: Political science, Handling Imbalanced Data in Classification Problems, FOS: Law, 02 engineering and technology, Epistemology, Pattern recognition (psychology), Detection and Prevention of Phishing Attacks, Selection (genetic algorithm), Engineering, Artificial Intelligence, Multi-label Text Classification in Machine Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Feature Selection, Multi-label Learning, Text Classification, Data mining, Political science, Cost-Sensitive Learning, Linguistics, Computer science, Process (computing), FOS: Philosophy, ethics and religion, Philosophy, Operating system, Spam Detection, Operations management, Computer Science, Physical Sciences, Feature selection, Quality (philosophy), FOS: Languages and literature, Relevance (law), Law, Information Systems

Cluster-based ensemble learning model for improving sentiment classification of Arabic documents

Autori: Rana Husni Al Mahmoud Bassam Hammo Hossam Faris

Zdroj: Natural Language Engineering. 30:1091-1129

Predmety: Artificial intelligence, Class (philosophy), Support vector machine, Handling Imbalanced Data in Classification Problems, 02 engineering and technology, Cluster analysis, Artificial Intelligence, Aspect-based Sentiment Analysis, Ensemble learning, Multi-label Text Classification in Machine Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Multi-label Learning, Ensemble Methods, 10. No inequality, Data mining, Cost-Sensitive Learning, Naive Bayes classifier, Imbalanced Data, 4. Education, Undersampling, Computer science, Sentiment Analysis and Opinion Mining, Computer Science, Physical Sciences, Random forest

Comparison and analysis of the machine learning in the movie subtitle document classification

Autori: Bill Chu Dongpo Xu Dongze Wu

Zdroj: Applied and Computational Engineering. 5:642-648

Predmety: Artificial intelligence, Natural language processing, 4. Education, Video Summarization, Automatic Video Summarization and Analysis, Computer science, Machine Learning Algorithms, Operating system, Sentiment Analysis and Opinion Mining, Artificial Intelligence, Computer Science, Physical Sciences, Multi-label Text Classification in Machine Learning, Machine learning, Information retrieval, Document Categorization, Computer Vision and Pattern Recognition, Multi-label Learning, Feature Selection, Subtitle, Classifier (UML)

Multi-label classification via closed frequent labelsets and label taxonomies

Autori: Mauri Ferrandin Ricardo Cerri

Zdroj: Soft Computing. 27:8627-8660

Predmety: Artificial intelligence, Support vector machine, Economics, Text Compression and Indexing Algorithms, Multi-label classification, 15. Life on land, Pattern recognition (psychology), Computer science, Directed acyclic graph, Algorithm, Multiclass classification, Artificial Intelligence, Hierarchy, Market economy, Multi-label Text Classification in Machine Learning, Computer Science, Physical Sciences, Shape Matching and Object Recognition, Machine learning, Computer Vision and Pattern Recognition, Multi-label Learning, Classifier (UML), Data mining

A combined analysis system for network products based on aspect extraction and sentiment classification

Autori: Shangzheng Song

Zdroj: Applied and Computational Engineering. 2:981-992

Predmety: FOS: Computer and information sciences, Artificial intelligence, Information extraction, Web Data Extraction, Set (abstract data type), Data science, 12. Responsible consumption, Web Data Extraction and Crawling Techniques, Sentiment analysis, Artificial Intelligence, Aspect-based Sentiment Analysis, Multi-label Text Classification in Machine Learning, Sentiment Analysis, Genetics, Information retrieval, Feature Selection, Data mining, Biology, Computer science, Process (computing), Programming language, Paragraph, World Wide Web, Operating system, Sentiment Analysis and Opinion Mining, Emotion Recognition, FOS: Biological sciences, Computer Science, Physical Sciences, Polarity (international relations), Cell, Information Systems

Research on Lovelorn Emotion Recognition Based on Ernie Tiny

Autori: Yuxin Huang

Zdroj: Frontiers in Computing and Intelligent Systems. 2:66-69

Predmety: Artificial intelligence, Text Mining, FOS: Political science, Happiness, Test (biology), Set (abstract data type), FOS: Law, Anger, Social psychology, Social media, Sentiment analysis, 03 medical and health sciences, 0302 clinical medicine, 5. Gender equality, Artificial Intelligence, Field (mathematics), Cognitive psychology, Multi-label Text Classification in Machine Learning, Sadness, FOS: Mathematics, Psychology, 10. No inequality, Public opinion, Political science, Biology, Natural language processing, 4. Education, Politics, Pure mathematics, Paleontology, Computer science, Programming language, 3. Good health, Automatic Keyword Extraction from Textual Data, FOS: Psychology, World Wide Web, Sentiment Analysis and Opinion Mining, Emotion Recognition, Computer Science, Physical Sciences, Emotion classification, Recall, Emotion recognition, Law, Mathematics