| TÃtulo : |
5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings |
| Tipo de documento: |
documento electrónico |
| Autores: |
Nicosia, Giuseppe, ; Pardalos, Panos, ; Umeton, Renato, ; Giuffrida, Giovanni, ; Sciacca, Vincenzo, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2019 |
| Número de páginas: |
XXVI, 772 p. 225 ilustraciones, 160 ilustraciones en color. |
| ISBN/ISSN/DL: |
978-3-030-37599-7 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Palabras clave: |
Software de la aplicacion Inteligencia artificial Procesamiento de datos Aplicaciones informáticas y de sistemas de información MinerÃa de datos y descubrimiento de conocimientos |
| Ãndice Dewey: |
005.3 Ciencia de los computadores (Programas) |
| Resumen: |
Este libro constituye las actas posteriores a la conferencia de la Quinta Conferencia Internacional sobre Aprendizaje Automático, Optimización y Ciencia de Datos, LOD 2019, celebrada en Siena, Italia, en septiembre de 2019. Los 54 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados entre 158 presentaciones. Los artÃculos cubren temas en el campo del aprendizaje automático, la inteligencia artificial, el aprendizaje por refuerzo, la optimización computacional y la ciencia de datos y presentan una variedad sustancial de ideas, tecnologÃas, algoritmos, métodos y aplicaciones. |
| Nota de contenido: |
Deep Neural Network Ensembles -- Driver Distraction Detection Using Deep Neural Network -- Deep Learning Algorithms for Complex Pattern Recognition in Ultrasonic Sensors Arrays -- An Information Analysis Approach into Feature Understanding of Convolutional Deep Neural Networks -- Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks -- Quantitative and Ontology-Based Comparison of Explanations for Image Classification -- About generative aspects of Variational Autoencoders -- Adapted Random Survival Forest for Histograms to Analyze NOx Sensor Failure in Heavy Trucks -- Incoherent submatrix selection via approximate independence sets in scalar product graphs -- LIA: A Label-Independent Algorithm for Feature Selection for Supervised Learning -- Relationship Estimation Metrics for Binary SoC Data -- Network Alignment using Graphlet Signature and High Order Proximity -- Effect of Market Spread over Reinforcement Learning based Market Maker -- A Beam Search for the LongestCommon Subsequence Problem Guided by a Novel Approximate Expected Length Calculation -- An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem -- The measure of regular relations recognition applied to the supervised classification task -- Simple and Accurate classifi cation method based on Class Association Rules performs well on well-known datasets -- Analyses of Multi-collection Corpora via Compound Topic Modeling -- Text mining with constrained tensor decomposition -- The induction problem: a machine learning vindication argument -- Geospatial Dimension in Association Rule Mining: The Case Study of the Amazon Charcoal Tree -- On Probabilistic k-Richness of the k-Means Algorithms -- Using clustering for supervised feature selection to detect relevant features -- A Structural Theorem for Center-Based Clustering in High-Dimensional Euclidean Space -- Modification of the k-MXT Algorithm and Its Application to the Geotagged Data Clustering -- CoPASample: A Heuristics based Covariance Preserving Data Augmentation -- Active Matrix Completion for Algorithm Selection -- A Framework for Multi- delity Modeling in Global Optimization Approaches -- Performance Evaluation of Local Surrogate Models in Bilevel Optimization -- BowTie - a deep learning feedforward neural network for sentiment analysis -- To What Extent Can Text Classifiation Help with Making Inferences About Students' Understanding -- Combinatorial Learning in Traffic Management -- Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits -- Designing an Optimal and Resilient iBGP Overlay with extended ORRTD -- GRASP Heuristics for the Stochastic Weighted Graph Fragmentation Problem -- Uniformly Most-Reliable Graphs and Antiholes -- Merging Quality Estimation for Binary Decision Diagrams with Binary Classfi ers -- Directed Acyclic Graph Reconstruction Leveraging Prior Partial Ordering Information -- Learning Scale and Shift-Invariant Dictionary for Sparse Representation -- Robust kernelized Bayesian matrix factorization for video background/foreground separation -- Parameter Optimization of Polynomial Kernel SVM from miniCV -- Analysing the Over t of the auto-sklearn Automated Machine Learning Tool -- A New Baseline for Automated Hyper-Parameter Optimization -- Optimal trade-o between sample size and precision of supervision for the xed effects panel data model -- Restaurant Health Inspections and Crime Statistics Predict the Real Estate Market in New York City -- Load Forecasting in District Heating Networks: Model Comparison on a Real-World Case Study -- A Chained Neural Network Model for Photovoltaic Power Forecast -- Trading-o Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels -- Designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size -- Multi-Task Learning by Pareto Optimality Nicosia -- Vital prognosis of patients in intensivecare units using an Ensemble of Bayesian Classifiers -- On the role of hub and orphan genes in the diagnosis of breast invasive carcinoma -- Approximating Probabilistic Constraints for Surgery Scheduling using Neural Networks -- Determining Principal Component Cardinality through the Principle of Minimum Description Length -- Modelling chaotic time series using recursive deep self-organising neural networks -- On Tree-based Methods for Similarity Learning -- Active Learning Approach for Safe Process Parameter Tuning -- Federated Learning of Deep Neural Decision Forests -- Data Anonymization for Privacy aware Machine Learning -- Exploiting Similar Behavior of Users in a Cooperative Optimization Approach for Distributing Service Points in Mobility Applications -- Long Short-Term Memory Networks for Earthquake Detection in Venezuelan Regions -- Zero-Shot Fashion Products Clustering on Social Image Streams -- Treating Arti cial Neural Net Training as a Nonsmooth Global Optimization Problem. |
| En lÃnea: |
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 |
5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings [documento electrónico] / Nicosia, Giuseppe, ; Pardalos, Panos, ; Umeton, Renato, ; Giuffrida, Giovanni, ; Sciacca, Vincenzo, . - 1 ed. . - [s.l.] : Springer, 2019 . - XXVI, 772 p. 225 ilustraciones, 160 ilustraciones en color. ISBN : 978-3-030-37599-7 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
| Palabras clave: |
Software de la aplicacion Inteligencia artificial Procesamiento de datos Aplicaciones informáticas y de sistemas de información MinerÃa de datos y descubrimiento de conocimientos |
| Ãndice Dewey: |
005.3 Ciencia de los computadores (Programas) |
| Resumen: |
Este libro constituye las actas posteriores a la conferencia de la Quinta Conferencia Internacional sobre Aprendizaje Automático, Optimización y Ciencia de Datos, LOD 2019, celebrada en Siena, Italia, en septiembre de 2019. Los 54 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados entre 158 presentaciones. Los artÃculos cubren temas en el campo del aprendizaje automático, la inteligencia artificial, el aprendizaje por refuerzo, la optimización computacional y la ciencia de datos y presentan una variedad sustancial de ideas, tecnologÃas, algoritmos, métodos y aplicaciones. |
| Nota de contenido: |
Deep Neural Network Ensembles -- Driver Distraction Detection Using Deep Neural Network -- Deep Learning Algorithms for Complex Pattern Recognition in Ultrasonic Sensors Arrays -- An Information Analysis Approach into Feature Understanding of Convolutional Deep Neural Networks -- Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks -- Quantitative and Ontology-Based Comparison of Explanations for Image Classification -- About generative aspects of Variational Autoencoders -- Adapted Random Survival Forest for Histograms to Analyze NOx Sensor Failure in Heavy Trucks -- Incoherent submatrix selection via approximate independence sets in scalar product graphs -- LIA: A Label-Independent Algorithm for Feature Selection for Supervised Learning -- Relationship Estimation Metrics for Binary SoC Data -- Network Alignment using Graphlet Signature and High Order Proximity -- Effect of Market Spread over Reinforcement Learning based Market Maker -- A Beam Search for the LongestCommon Subsequence Problem Guided by a Novel Approximate Expected Length Calculation -- An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem -- The measure of regular relations recognition applied to the supervised classification task -- Simple and Accurate classifi cation method based on Class Association Rules performs well on well-known datasets -- Analyses of Multi-collection Corpora via Compound Topic Modeling -- Text mining with constrained tensor decomposition -- The induction problem: a machine learning vindication argument -- Geospatial Dimension in Association Rule Mining: The Case Study of the Amazon Charcoal Tree -- On Probabilistic k-Richness of the k-Means Algorithms -- Using clustering for supervised feature selection to detect relevant features -- A Structural Theorem for Center-Based Clustering in High-Dimensional Euclidean Space -- Modification of the k-MXT Algorithm and Its Application to the Geotagged Data Clustering -- CoPASample: A Heuristics based Covariance Preserving Data Augmentation -- Active Matrix Completion for Algorithm Selection -- A Framework for Multi- delity Modeling in Global Optimization Approaches -- Performance Evaluation of Local Surrogate Models in Bilevel Optimization -- BowTie - a deep learning feedforward neural network for sentiment analysis -- To What Extent Can Text Classifiation Help with Making Inferences About Students' Understanding -- Combinatorial Learning in Traffic Management -- Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits -- Designing an Optimal and Resilient iBGP Overlay with extended ORRTD -- GRASP Heuristics for the Stochastic Weighted Graph Fragmentation Problem -- Uniformly Most-Reliable Graphs and Antiholes -- Merging Quality Estimation for Binary Decision Diagrams with Binary Classfi ers -- Directed Acyclic Graph Reconstruction Leveraging Prior Partial Ordering Information -- Learning Scale and Shift-Invariant Dictionary for Sparse Representation -- Robust kernelized Bayesian matrix factorization for video background/foreground separation -- Parameter Optimization of Polynomial Kernel SVM from miniCV -- Analysing the Over t of the auto-sklearn Automated Machine Learning Tool -- A New Baseline for Automated Hyper-Parameter Optimization -- Optimal trade-o between sample size and precision of supervision for the xed effects panel data model -- Restaurant Health Inspections and Crime Statistics Predict the Real Estate Market in New York City -- Load Forecasting in District Heating Networks: Model Comparison on a Real-World Case Study -- A Chained Neural Network Model for Photovoltaic Power Forecast -- Trading-o Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels -- Designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size -- Multi-Task Learning by Pareto Optimality Nicosia -- Vital prognosis of patients in intensivecare units using an Ensemble of Bayesian Classifiers -- On the role of hub and orphan genes in the diagnosis of breast invasive carcinoma -- Approximating Probabilistic Constraints for Surgery Scheduling using Neural Networks -- Determining Principal Component Cardinality through the Principle of Minimum Description Length -- Modelling chaotic time series using recursive deep self-organising neural networks -- On Tree-based Methods for Similarity Learning -- Active Learning Approach for Safe Process Parameter Tuning -- Federated Learning of Deep Neural Decision Forests -- Data Anonymization for Privacy aware Machine Learning -- Exploiting Similar Behavior of Users in a Cooperative Optimization Approach for Distributing Service Points in Mobility Applications -- Long Short-Term Memory Networks for Earthquake Detection in Venezuelan Regions -- Zero-Shot Fashion Products Clustering on Social Image Streams -- Treating Arti cial Neural Net Training as a Nonsmooth Global Optimization Problem. |
| En lÃnea: |
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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