| Título : |
European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III |
| Tipo de documento: |
documento electrónico |
| Autores: |
Brefeld, Ulf, ; Curry, Edward, ; Daly, Elizabeth, ; MacNamee, Brian, ; Marascu, Alice, ; Pinelli, Fabio, ; Berlingerio, Michele, ; Hurley, Neil, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2019 |
| Número de páginas: |
XXXI, 706 p. 332 ilustraciones, 194 ilustraciones en color. |
| ISBN/ISSN/DL: |
978-3-030-10997-4 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Palabras clave: |
Procesamiento de datos Inteligencia artificial Ingeniería Informática Red de computadoras Ciencias sociales Protección de datos Delitos informáticos Minería de datos y descubrimiento de conocimientos Ingeniería Informática y Redes Aplicación informática en ciencias sociales y del comportamiento Seguridad de datos e información Crimen informático |
| Índice Dewey: |
6.312 |
| Resumen: |
Las actas de tres volúmenes LNAI 11051 – 11053 constituyen las actas arbitradas de la Conferencia Europea sobre Aprendizaje Automático y Descubrimiento de Conocimiento en Bases de Datos, ECML PKDD 2018, celebrada en Dublín, Irlanda, en septiembre de 2018. El total de 131 artículos regulares presentados en la parte I y la parte II fue cuidadosamente revisada y seleccionada entre 535 presentaciones; Hay 52 artículos en la sección de demostración, néctar y ciencia de datos aplicada. Las contribuciones se organizaron en secciones temáticas denominadas de la siguiente manera: Parte I: aprendizaje contradictorio; detección de anomalías y valores atípicos; aplicaciones; clasificación; agrupamiento y aprendizaje no supervisado; aprendizaje profundo; métodos de conjunto; y evaluación. Parte II: gráficos; métodos del núcleo; paradigmas de aprendizaje; análisis matricial y tensorial; aprendizaje activo y en línea; minería de patrones y secuencias; modelos probabilísticos y métodos estadísticos; sistemas de recomendación; y transferir el aprendizaje. Parte III: Aplicaciones de ciencia de datos de ADS; Comercio electrónico de ADS; Ingeniería y diseño de ADS; ADS financieros y de seguridad; salud de los anuncios; detección y posicionamiento de ADS; pista de néctar; y pista de demostración. |
| Nota de contenido: |
ADS Data Science Applications -- Neural Article Pair Modeling for Wikipedia Sub-article Matching -- LinNet: Probabilistic Lineup Evaluation Through Network Embedding -- Improving Emotion Detection with Sub-clip Boosting -- Machine Learning for Targeted Assimilation of Satellite Data -- From Empirical Analysis to Public Policy: Evaluating Housing Systems for Homeless Youth -- Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping -- ADS E-commerce -- SPEEDING up the Metabolism in E-commerce by Reinforcement Mechanism DESIGN -- Intent-aware Audience Targeting for Ride-hailing Service -- A Recurrent Neural Network Survival Model: Predicting Web User Return Time -- Implicit Linking of Food Entities in Social Media -- A Practical Deep Online Ranking System in E-commerce Recommendation -- ADS Engineering and Design -- ST-DenNetFus: A New Deep Learning Approach for Network Demand Prediction -- Automating Layout Synthesis with Constructive Preference Elicitation -- Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems -- Learning Cheap and Novel Flight Itineraries -- Towards Resource-Efficient Classifiers for Always-On Monitoring -- ADS Financial / Security -- Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series -- Using Reinforcement Learning to Conceal Honeypot Functionality -- Flexible Inference for Cyberbully Incident Detection -- Solving the \false positives" problem in fraud prediction - Automated Data Science at an Industrial Scale -- Learning Tensor-based Representations from Brain-Computer Interface Data for Cybersecurity -- ADS Health -- Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation -- AMIE: Automatic Monitoring of Indoor Exercises -- Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction -- Selecting Influenza Mitigation Strategies Using Bayesian Bandits -- Hypotensive Episode Prediction in ICUs via Observation Window Splitting -- Equipment Health Indicator Learning using Deep Reinforcement Learning -- ADS Sensing/Positioning -- PBE: Driver Behavior Assessment Beyond Trajectory Profiling -- Accurate WiFi-based Indoor Positioning with Continuous Location Sampling -- Human Activity Recognition with Convolutional Neural Networks -- Urban sensing for anomalous event detection -- Combining Bayesian Inference and Clustering for Transport Mode Detection from Sparse and Noisy Geolocation Data -- CentroidNet: A Deep Neural Network for Joint Object Localization and Counting -- Deep Modular Multimodal Fusion on Multiple Sensors for Volcano Activity Recognition -- Nectar Track -- Matrix Completion under Interval Uncertainty -- A two-step approach for the prediction of mood levels based on diary data -- Best Practices to Train Deep Models on Imbalanced Datasets - A Case Study on Animal Detection in Aerial Imagery -- Deep Query Ranking for Question Answering over Knowledge Bases -- Machine Learning Approaches to Hybrid Music Recommender Systems -- Demo Track -- IDEA: An Interactive Dialogue Translation Demo System Using Furhat Robots -- RAPID: Real-time Analytics Platform for Interactive Data Mining -- COBRASTS: A new approach to Semi-Supervised Clustering of Time Series -- pysubgroup: Easy-to-use Subgroup Discovery in Python -- An Advert Creation System for Next-Gen Publicity -- VHI : Valve Health Identification for the Maintenance of Subsea Industrial Equipment -- Tiler: Software for Human-Guided Data Exploration -- ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio -- ClaRe: Classification and Regression Tool for Multivariate Time Series -- Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning -- Monitoring Emergency First Responders' Activities via Gradient Boosting and Inertial Sensor Data -- Visualizing Multi-Document Semantics via Open Domain Information Extraction. |
| 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 |
European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III [documento electrónico] / Brefeld, Ulf, ; Curry, Edward, ; Daly, Elizabeth, ; MacNamee, Brian, ; Marascu, Alice, ; Pinelli, Fabio, ; Berlingerio, Michele, ; Hurley, Neil, . - 1 ed. . - [s.l.] : Springer, 2019 . - XXXI, 706 p. 332 ilustraciones, 194 ilustraciones en color. ISBN : 978-3-030-10997-4 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
| Palabras clave: |
Procesamiento de datos Inteligencia artificial Ingeniería Informática Red de computadoras Ciencias sociales Protección de datos Delitos informáticos Minería de datos y descubrimiento de conocimientos Ingeniería Informática y Redes Aplicación informática en ciencias sociales y del comportamiento Seguridad de datos e información Crimen informático |
| Índice Dewey: |
6.312 |
| Resumen: |
Las actas de tres volúmenes LNAI 11051 – 11053 constituyen las actas arbitradas de la Conferencia Europea sobre Aprendizaje Automático y Descubrimiento de Conocimiento en Bases de Datos, ECML PKDD 2018, celebrada en Dublín, Irlanda, en septiembre de 2018. El total de 131 artículos regulares presentados en la parte I y la parte II fue cuidadosamente revisada y seleccionada entre 535 presentaciones; Hay 52 artículos en la sección de demostración, néctar y ciencia de datos aplicada. Las contribuciones se organizaron en secciones temáticas denominadas de la siguiente manera: Parte I: aprendizaje contradictorio; detección de anomalías y valores atípicos; aplicaciones; clasificación; agrupamiento y aprendizaje no supervisado; aprendizaje profundo; métodos de conjunto; y evaluación. Parte II: gráficos; métodos del núcleo; paradigmas de aprendizaje; análisis matricial y tensorial; aprendizaje activo y en línea; minería de patrones y secuencias; modelos probabilísticos y métodos estadísticos; sistemas de recomendación; y transferir el aprendizaje. Parte III: Aplicaciones de ciencia de datos de ADS; Comercio electrónico de ADS; Ingeniería y diseño de ADS; ADS financieros y de seguridad; salud de los anuncios; detección y posicionamiento de ADS; pista de néctar; y pista de demostración. |
| Nota de contenido: |
ADS Data Science Applications -- Neural Article Pair Modeling for Wikipedia Sub-article Matching -- LinNet: Probabilistic Lineup Evaluation Through Network Embedding -- Improving Emotion Detection with Sub-clip Boosting -- Machine Learning for Targeted Assimilation of Satellite Data -- From Empirical Analysis to Public Policy: Evaluating Housing Systems for Homeless Youth -- Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping -- ADS E-commerce -- SPEEDING up the Metabolism in E-commerce by Reinforcement Mechanism DESIGN -- Intent-aware Audience Targeting for Ride-hailing Service -- A Recurrent Neural Network Survival Model: Predicting Web User Return Time -- Implicit Linking of Food Entities in Social Media -- A Practical Deep Online Ranking System in E-commerce Recommendation -- ADS Engineering and Design -- ST-DenNetFus: A New Deep Learning Approach for Network Demand Prediction -- Automating Layout Synthesis with Constructive Preference Elicitation -- Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems -- Learning Cheap and Novel Flight Itineraries -- Towards Resource-Efficient Classifiers for Always-On Monitoring -- ADS Financial / Security -- Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series -- Using Reinforcement Learning to Conceal Honeypot Functionality -- Flexible Inference for Cyberbully Incident Detection -- Solving the \false positives" problem in fraud prediction - Automated Data Science at an Industrial Scale -- Learning Tensor-based Representations from Brain-Computer Interface Data for Cybersecurity -- ADS Health -- Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation -- AMIE: Automatic Monitoring of Indoor Exercises -- Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction -- Selecting Influenza Mitigation Strategies Using Bayesian Bandits -- Hypotensive Episode Prediction in ICUs via Observation Window Splitting -- Equipment Health Indicator Learning using Deep Reinforcement Learning -- ADS Sensing/Positioning -- PBE: Driver Behavior Assessment Beyond Trajectory Profiling -- Accurate WiFi-based Indoor Positioning with Continuous Location Sampling -- Human Activity Recognition with Convolutional Neural Networks -- Urban sensing for anomalous event detection -- Combining Bayesian Inference and Clustering for Transport Mode Detection from Sparse and Noisy Geolocation Data -- CentroidNet: A Deep Neural Network for Joint Object Localization and Counting -- Deep Modular Multimodal Fusion on Multiple Sensors for Volcano Activity Recognition -- Nectar Track -- Matrix Completion under Interval Uncertainty -- A two-step approach for the prediction of mood levels based on diary data -- Best Practices to Train Deep Models on Imbalanced Datasets - A Case Study on Animal Detection in Aerial Imagery -- Deep Query Ranking for Question Answering over Knowledge Bases -- Machine Learning Approaches to Hybrid Music Recommender Systems -- Demo Track -- IDEA: An Interactive Dialogue Translation Demo System Using Furhat Robots -- RAPID: Real-time Analytics Platform for Interactive Data Mining -- COBRASTS: A new approach to Semi-Supervised Clustering of Time Series -- pysubgroup: Easy-to-use Subgroup Discovery in Python -- An Advert Creation System for Next-Gen Publicity -- VHI : Valve Health Identification for the Maintenance of Subsea Industrial Equipment -- Tiler: Software for Human-Guided Data Exploration -- ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio -- ClaRe: Classification and Regression Tool for Multivariate Time Series -- Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning -- Monitoring Emergency First Responders' Activities via Gradient Boosting and Inertial Sensor Data -- Visualizing Multi-Document Semantics via Open Domain Information Extraction. |
| 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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