| Título : |
23rd International Conference, DS 2020, Thessaloniki, Greece, October 19–21, 2020, Proceedings |
| Tipo de documento: |
documento electrónico |
| Autores: |
Appice, Annalisa, ; Tsoumakas, Grigorios, ; Manolopoulos, Yannis, ; Matwin, Stan, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2020 |
| Número de páginas: |
XXI, 706 p. 227 ilustraciones, 147 ilustraciones en color. |
| ISBN/ISSN/DL: |
978-3-030-61527-7 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Palabras clave: |
Inteligencia artificial Software de la aplicacion Procesamiento de datos Sistemas de almacenamiento y recuperación de información Aplicaciones informáticas y de sistemas de información Computadoras y Educación Minería de datos y descubrimiento de conocimientos Almacenamiento y recuperación de información |
| Índice Dewey: |
006.3 Inteligencia artificial |
| Resumen: |
Este libro constituye las actas de la 23.ª Conferencia Internacional sobre Descubrimiento de Ciencias, DS 2020, que tuvo lugar del 19 al 21 de octubre de 2020. La conferencia estaba prevista para realizarse en Salónica, Grecia, pero tuvo que cambiar a un formato en línea debido a la pandemia de COVID-19. Los 26 artículos completos y 19 artículos breves presentados en este volumen fueron cuidadosamente revisados y seleccionados entre 76 presentaciones. Las contribuciones se organizaron en secciones temáticas denominadas: clasificación; agrupamiento; representación de datos y conocimientos; flujos de datos; procesamiento distribuido; conjuntos; aprendizaje automático explicable e interpretable; minería de gráficos y redes; modelos de objetivos múltiples; redes neuronales y aprendizaje profundo; y datos espaciales, temporales y espaciotemporales. |
| Nota de contenido: |
Classification -- Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach -- Mitigating Discrimination in Clinical Machine Learning Decision Support using Algorithmic Processing Techniques -- WeakAL: Combining Active Learning and Weak Supervision -- Clustering -- Constrained Clustering via Post-Processing -- Deep Convolutional Embedding for Painting Clustering: Case Study on Picasso's Artworks -- Dynamic Incremental Semi-Supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction -- Iterative Multi-Mode Discretization: Applications to Co-Clustering -- Data and Knowledge Representation -- COVID-19 Therapy Target Discovery with Context-aware Literature Mining -- Semantic Annotation of Predictive Modelling Experiments -- Semantic Description of Data Mining Datasets: An Ontology-based Annotation Schema -- Data Streams -- FABBOO - Online Fairness-aware Learning under Class Imbalance -- FEAT: A Fairness-enhancing andConcept-adapting Decision Tree Classifer -- Unsupervised Concept Drift Detection using a Student{Teacher Approach -- Dimensionality Reduction and Feature Selection -- Assembled Feature Selection For Credit Scoring in Micro nance With Non-Traditional Features -- Learning Surrogates of a Radiative Transfer Model for the Sentinel 5P Satellite -- Nets versus Trees for Feature Ranking and Gene Network Inference -- Pathway Activity Score Learning Algorithm for Dimensionality Reduction of Gene Expression Data -- Machine learning for Modelling and Understanding in Earth Sciences -- Distributed Processing -- Balancing between Scalability and Accuracy in Time-Series Classification for Stream and Batch Settings -- DeCStor: A Framework for Privately and Securely Sharing Files Using a Public Blockchain -- Investigating Parallelization of MAML -- Ensembles -- Extreme Algorithm Selection with Dyadic Feature Representation -- Federated Ensemble Regression using Classification -- One-Class Ensembles for Rare Genomic Sequences Identification -- Explainable and Interpretable Machine Learning -- Explaining Sentiment Classi cation with Synthetic Exemplars and Counter-Exemplars -- Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology -- Interpretable Machine Learning with Bitonic Generalized Additive Models and Automatic Feature Construction -- Predicting and Explaining Privacy Risk Exposure in Mobility Data -- Graph and Network Mining -- Maximizing Network Coverage Under the Presence of Time Constraint by Injecting Most Effective k-Links -- On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: a Link Prediction Perspective -- Simultaneous Process Drift Detection and Characterization with Pattern-based Change Detectors -- Multi-Target Models -- Extreme Gradient Boosted Multi-label Trees for Dynamic ClassifierChains -- Hierarchy Decomposition Pipeline: A Toolbox for Comparison of Model Induction Algorithms on Hierarchical Multi-label Classification Problems -- Missing Value Imputation with MERCS: a Faster Alternative to MissForest -- Multi-Directional Rule Set Learning -- On Aggregation in Ensembles of Multilabel Classifiers -- Neural Networks and Deep Learning -- Attention in Recurrent Neural Networks for Energy Disaggregation -- Enhanced Food Safety Through Deep Learning for Food Recalls Prediction -- Machine learning for Modelling and Understanding in Earth Sciences -- FairNN - Conjoint Learning of Fair Representations for Fair Decisions -- Improving Deep Unsupervised Anomaly Detection by Exploiting VAE Latent Space Distribution -- Spatial, Temporal and Spatiotemporal Data -- Detecting Temporal Anomalies in Business Processes using Distance-based Methods -- Mining Constrained Regions of Interest: An Optimization Approach -- Mining Disjoint Sequential Pattern Pairs from Tourist Trajectory Data -- Predicting the Health Condition of mHealth App Users with Large Differences in the Amount of Recorded Observations - Where to Learn from -- Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method -- Time Series Regression in Professional Road Cycling. |
| 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 |
23rd International Conference, DS 2020, Thessaloniki, Greece, October 19–21, 2020, Proceedings [documento electrónico] / Appice, Annalisa, ; Tsoumakas, Grigorios, ; Manolopoulos, Yannis, ; Matwin, Stan, . - 1 ed. . - [s.l.] : Springer, 2020 . - XXI, 706 p. 227 ilustraciones, 147 ilustraciones en color. ISBN : 978-3-030-61527-7 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
| Palabras clave: |
Inteligencia artificial Software de la aplicacion Procesamiento de datos Sistemas de almacenamiento y recuperación de información Aplicaciones informáticas y de sistemas de información Computadoras y Educación Minería de datos y descubrimiento de conocimientos Almacenamiento y recuperación de información |
| Índice Dewey: |
006.3 Inteligencia artificial |
| Resumen: |
Este libro constituye las actas de la 23.ª Conferencia Internacional sobre Descubrimiento de Ciencias, DS 2020, que tuvo lugar del 19 al 21 de octubre de 2020. La conferencia estaba prevista para realizarse en Salónica, Grecia, pero tuvo que cambiar a un formato en línea debido a la pandemia de COVID-19. Los 26 artículos completos y 19 artículos breves presentados en este volumen fueron cuidadosamente revisados y seleccionados entre 76 presentaciones. Las contribuciones se organizaron en secciones temáticas denominadas: clasificación; agrupamiento; representación de datos y conocimientos; flujos de datos; procesamiento distribuido; conjuntos; aprendizaje automático explicable e interpretable; minería de gráficos y redes; modelos de objetivos múltiples; redes neuronales y aprendizaje profundo; y datos espaciales, temporales y espaciotemporales. |
| Nota de contenido: |
Classification -- Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach -- Mitigating Discrimination in Clinical Machine Learning Decision Support using Algorithmic Processing Techniques -- WeakAL: Combining Active Learning and Weak Supervision -- Clustering -- Constrained Clustering via Post-Processing -- Deep Convolutional Embedding for Painting Clustering: Case Study on Picasso's Artworks -- Dynamic Incremental Semi-Supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction -- Iterative Multi-Mode Discretization: Applications to Co-Clustering -- Data and Knowledge Representation -- COVID-19 Therapy Target Discovery with Context-aware Literature Mining -- Semantic Annotation of Predictive Modelling Experiments -- Semantic Description of Data Mining Datasets: An Ontology-based Annotation Schema -- Data Streams -- FABBOO - Online Fairness-aware Learning under Class Imbalance -- FEAT: A Fairness-enhancing andConcept-adapting Decision Tree Classifer -- Unsupervised Concept Drift Detection using a Student{Teacher Approach -- Dimensionality Reduction and Feature Selection -- Assembled Feature Selection For Credit Scoring in Micro nance With Non-Traditional Features -- Learning Surrogates of a Radiative Transfer Model for the Sentinel 5P Satellite -- Nets versus Trees for Feature Ranking and Gene Network Inference -- Pathway Activity Score Learning Algorithm for Dimensionality Reduction of Gene Expression Data -- Machine learning for Modelling and Understanding in Earth Sciences -- Distributed Processing -- Balancing between Scalability and Accuracy in Time-Series Classification for Stream and Batch Settings -- DeCStor: A Framework for Privately and Securely Sharing Files Using a Public Blockchain -- Investigating Parallelization of MAML -- Ensembles -- Extreme Algorithm Selection with Dyadic Feature Representation -- Federated Ensemble Regression using Classification -- One-Class Ensembles for Rare Genomic Sequences Identification -- Explainable and Interpretable Machine Learning -- Explaining Sentiment Classi cation with Synthetic Exemplars and Counter-Exemplars -- Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology -- Interpretable Machine Learning with Bitonic Generalized Additive Models and Automatic Feature Construction -- Predicting and Explaining Privacy Risk Exposure in Mobility Data -- Graph and Network Mining -- Maximizing Network Coverage Under the Presence of Time Constraint by Injecting Most Effective k-Links -- On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: a Link Prediction Perspective -- Simultaneous Process Drift Detection and Characterization with Pattern-based Change Detectors -- Multi-Target Models -- Extreme Gradient Boosted Multi-label Trees for Dynamic ClassifierChains -- Hierarchy Decomposition Pipeline: A Toolbox for Comparison of Model Induction Algorithms on Hierarchical Multi-label Classification Problems -- Missing Value Imputation with MERCS: a Faster Alternative to MissForest -- Multi-Directional Rule Set Learning -- On Aggregation in Ensembles of Multilabel Classifiers -- Neural Networks and Deep Learning -- Attention in Recurrent Neural Networks for Energy Disaggregation -- Enhanced Food Safety Through Deep Learning for Food Recalls Prediction -- Machine learning for Modelling and Understanding in Earth Sciences -- FairNN - Conjoint Learning of Fair Representations for Fair Decisions -- Improving Deep Unsupervised Anomaly Detection by Exploiting VAE Latent Space Distribution -- Spatial, Temporal and Spatiotemporal Data -- Detecting Temporal Anomalies in Business Processes using Distance-based Methods -- Mining Constrained Regions of Interest: An Optimization Approach -- Mining Disjoint Sequential Pattern Pairs from Tourist Trajectory Data -- Predicting the Health Condition of mHealth App Users with Large Differences in the Amount of Recorded Observations - Where to Learn from -- Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method -- Time Series Regression in Professional Road Cycling. |
| 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 |
|  |
Hacer una sugerencia Refinar búsqueda23rd International Conference, DS 2020, Thessaloniki, Greece, October 19–21, 2020, Proceedings / Appice, Annalisa ; Tsoumakas, Grigorios ; Manolopoulos, Yannis ; Matwin, Stan
