19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26–28, 2021, Proceedings / Abreu, Pedro Henriques ; Rodrigues, Pedro Pereira ; Fernández, Alberto ; Gama, João  

Público ISBD Ningún comentario para este registro. Título : 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26–28, 2021, Proceedings Tipo de documento: documento electrónico Autores: Abreu, Pedro Henriques, ; Rodrigues, Pedro Pereira, ; Fernández, Alberto, ; Gama, João, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XVI, 454 p. 138 ilustraciones, 107 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-74251-5 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Gestión de base de datos  Ciencias sociales  Algoritmos  Procesamiento del lenguaje natural (Informática)  Aplicación informática en ciencias sociales y del comportamiento  Diseño y Análisis de Algoritmos  Computadoras y Educación  Procesamiento del lenguaje natural (PNL) Índice Dewey: 005.74 Ciencia de los computadores (Archivos de datos y bases de datos) Resumen: Este libro constituye las actas del 19º Simposio Internacional sobre Análisis Inteligente de Datos, IDA 2021, que estaba previsto que tuviera lugar en Oporto, Portugal. Debido a la pandemia de COVID-19, la conferencia se llevó a cabo en línea del 26 al 28 de abril de 2021. Los 35 artículos incluidos en este libro fueron cuidadosamente revisados ​​y seleccionados entre 113 presentaciones. Los artículos se organizaron en secciones temáticas denominadas: modelado con redes neuronales; modelado con aprendizaje estadístico; lenguaje de modelado y gráficos; y modelado de formatos de datos especiales. Nota de contenido: Modeling with Neural Networks -- Hyperspherical Weight Uncertainty in Neural Networks -- Partially Monotonic Learning for Neural Networks -- Multiple-Manifold Generation with an Ensemble GAN and Learned Noise Prior -- Simple, Efficient and Convenient Decentralized Multi-Task Learning for Neural Networks -- Deep Hybrid Neural Networks with Improved Weighted Word Embeddings for Sentiment Analysis -- Explaining Neural Networks by Decoding Layer Activations -- Analogical Embedding for Analogy-based Learning to Rank -- HORUS-NER: A Multimodal Named Entity Recognition Framework for Noisy Data -- Modeling with Statistical Learning -- Incremental Search Space Construction for Machine Learning Pipeline Synthesis -- Adversarial Vulnerability of Active Transfer Learning -- Revisiting Non-Specific Syndromic Surveillance -- Gradient Ascent for Best Response Regression -- Intelligent Structural Damage Detection: a Federated Learning Approach -- Composite surrogate for likelihood-freeBayesian optimisation in high-dimensional settings of activity-based transportation models -- Active Selection of Classification Features -- Feature Selection for Hierarchical Multi-Label Classification -- Bandit Algorithm for Both Unknown Best Position and Best Item Display on Web Pages -- Performance prediction for hardware-software configurations: A case study for video games -- avatar / Automated Feature Wrangling for Machine Learning -- Modeling Language and Graphs -- Semantically Enriching Embeddings of Highly In ectable Verbs for Improving Intent Detection in a Romanian Home Assistant Scenario -- BoneBert: A BERT-based Automated Information Extraction System of Radiology Reports for Bone Fracture Detection and Diagnosis -- Linking the Dynamics of User Stance to the Structure of Online Discussions -- Unsupervised Methods for the Study of Transformer Embeddings -- A Framework for Authorial Clustering of Shorter Texts in Latent Semantic Spaces -- DeepGG: a Deep Graph Generator -- SINr: fast computing of Sparse Interpretable Node Representations is not a sin -- Detection of contextual anomalies in attributed graphs -- Ising-Based Louvain Method: Clustering Large Graphs with Specialized Hardware -- Modeling Special Data Formats -- Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules -- Z-Hist: A Temporal Abstraction of Multivariate Histogram Snapshots -- muppets: Multipurpose Table Segmentation -- SpLyCI: Integrating Spreadsheets by Recognising and Solving Layout Constraints -- RTL: A Robust Time Series Labeling Algorithm -- The Compromise of Data Privacy in Predictive Performance -- Efficient Privacy Preserving Distributed K-Means for Non-IID Data. En línea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26–28, 2021, Proceedings [documento electrónico] / Abreu, Pedro Henriques, ; Rodrigues, Pedro Pereira, ; Fernández, Alberto, ; Gama, João,  . -  1 ed.  . - [s.l.] : Springer, 2021 . - XVI, 454 p. 138 ilustraciones, 107 ilustraciones en color. ISBN : 978-3-030-74251-5 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Palabras clave: Gestión de base de datos  Ciencias sociales  Algoritmos  Procesamiento del lenguaje natural (Informática)  Aplicación informática en ciencias sociales y del comportamiento  Diseño y Análisis de Algoritmos  Computadoras y Educación  Procesamiento del lenguaje natural (PNL) Índice Dewey: 005.74 Ciencia de los computadores (Archivos de datos y bases de datos) Resumen: Este libro constituye las actas del 19º Simposio Internacional sobre Análisis Inteligente de Datos, IDA 2021, que estaba previsto que tuviera lugar en Oporto, Portugal. Debido a la pandemia de COVID-19, la conferencia se llevó a cabo en línea del 26 al 28 de abril de 2021. Los 35 artículos incluidos en este libro fueron cuidadosamente revisados ​​y seleccionados entre 113 presentaciones. Los artículos se organizaron en secciones temáticas denominadas: modelado con redes neuronales; modelado con aprendizaje estadístico; lenguaje de modelado y gráficos; y modelado de formatos de datos especiales. Nota de contenido: Modeling with Neural Networks -- Hyperspherical Weight Uncertainty in Neural Networks -- Partially Monotonic Learning for Neural Networks -- Multiple-Manifold Generation with an Ensemble GAN and Learned Noise Prior -- Simple, Efficient and Convenient Decentralized Multi-Task Learning for Neural Networks -- Deep Hybrid Neural Networks with Improved Weighted Word Embeddings for Sentiment Analysis -- Explaining Neural Networks by Decoding Layer Activations -- Analogical Embedding for Analogy-based Learning to Rank -- HORUS-NER: A Multimodal Named Entity Recognition Framework for Noisy Data -- Modeling with Statistical Learning -- Incremental Search Space Construction for Machine Learning Pipeline Synthesis -- Adversarial Vulnerability of Active Transfer Learning -- Revisiting Non-Specific Syndromic Surveillance -- Gradient Ascent for Best Response Regression -- Intelligent Structural Damage Detection: a Federated Learning Approach -- Composite surrogate for likelihood-freeBayesian optimisation in high-dimensional settings of activity-based transportation models -- Active Selection of Classification Features -- Feature Selection for Hierarchical Multi-Label Classification -- Bandit Algorithm for Both Unknown Best Position and Best Item Display on Web Pages -- Performance prediction for hardware-software configurations: A case study for video games -- avatar / Automated Feature Wrangling for Machine Learning -- Modeling Language and Graphs -- Semantically Enriching Embeddings of Highly In ectable Verbs for Improving Intent Detection in a Romanian Home Assistant Scenario -- BoneBert: A BERT-based Automated Information Extraction System of Radiology Reports for Bone Fracture Detection and Diagnosis -- Linking the Dynamics of User Stance to the Structure of Online Discussions -- Unsupervised Methods for the Study of Transformer Embeddings -- A Framework for Authorial Clustering of Shorter Texts in Latent Semantic Spaces -- DeepGG: a Deep Graph Generator -- SINr: fast computing of Sparse Interpretable Node Representations is not a sin -- Detection of contextual anomalies in attributed graphs -- Ising-Based Louvain Method: Clustering Large Graphs with Specialized Hardware -- Modeling Special Data Formats -- Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules -- Z-Hist: A Temporal Abstraction of Multivariate Histogram Snapshots -- muppets: Multipurpose Table Segmentation -- SpLyCI: Integrating Spreadsheets by Recognising and Solving Layout Constraints -- RTL: A Robust Time Series Labeling Algorithm -- The Compromise of Data Privacy in Predictive Performance -- Efficient Privacy Preserving Distributed K-Means for Non-IID Data. En línea: https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Link: https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i

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