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Autor Vens, Celine |
Documentos disponibles escritos por este autor (2)
Crear una solicitud de compra Refinar búsquedaMachine Learning and Knowledge Discovery in Databases / Ceci, Michelangelo ; Hollmén, Jaakko ; Todorovski, LjupÄo ; Vens, Celine ; Džeroski, SaÅ¡o
TÃtulo : Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part I / Tipo de documento: documento electrónico Autores: Ceci, Michelangelo, ; Hollmén, Jaakko, ; Todorovski, LjupÄo, ; Vens, Celine, ; Džeroski, SaÅ¡o, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: LXIII, 852 p. 245 ilustraciones ISBN/ISSN/DL: 978-3-319-71249-9 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Procesamiento de datos Inteligencia artificial Visión por computador Software de la aplicacion Protección de datos Ordenadores MinerÃa de datos y descubrimiento de conocimientos Aplicaciones informáticas y de sistemas de información Seguridad de datos e información Entornos informáticos Clasificación: 6.312 Resumen: Las actas de tres volúmenes LNAI 10534 – 10536 constituyen las actas arbitradas de la Conferencia Europea sobre Aprendizaje Automático y Descubrimiento de Conocimiento en Bases de Datos, ECML PKDD 2017, celebrada en Skopje, Macedonia, en septiembre de 2017. El total de 101 artÃculos regulares presentados en la parte I y la parte II fue cuidadosamente revisada y seleccionada entre 364 presentaciones; Hay 47 artÃculos 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: detección de anomalÃas; visión por computador; conjuntos y metaaprendizaje; selección y extracción de caracterÃsticas; métodos del núcleo; aprendizaje y optimización, factorización matricial y tensorial; redes y gráficos; Redes neuronales y aprendizaje profundo. Parte II: minerÃa de patrones y secuencias; privacidad y seguridad; modelos y métodos probabilÃsticos; recomendación; regresión; aprendizaje reforzado; descubrimiento de subgrupos; series de tiempo y flujos; transferencia y aprendizaje multitarea; Aprendizaje no supervisado y semisupervisado. Parte III: pista de ciencia de datos aplicada; pista de néctar; y pista de demostración. Nota de contenido: Anomaly Detection -- Concentration Free Outlier Detection -- Efficient top rank optimization with gradient boosting for supervised anomaly detection -- Robust, Deep and Inductive Anomaly Detection -- Sentiment Informed Cyberbullying Detection in Social Media -- zooRank: Ranking Suspicious Activities in Time-Evolving Tensors -- Computer Vision -- Alternative Semantic Representations for Zero-Shot Human Action Recognition -- Early Active Learning with Pairwise Constraint for Person Re-identification -- Guiding InfoGAN with Semi-Supervision -- Scatteract: Automated extraction of data from scatter plots -- Unsupervised Diverse Colorization via Generative Adversarial Networks -- Ensembles and Meta Learning -- Dynamic Ensemble Selection with Probabilistic Classifier Chains -- Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks -- Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks -- Feature Selection and Extraction -- Deep Discrete Hashing with Self-supervised Labels -- Including multi-feature interactions and redundancy for feature ranking in mixed datasets -- Non-redundant Spectral Dimensionality Reduction -- Rethinking Unsupervised Feature Selection: From Pseudo Labels to Pseudo Must-links -- SetExpan: Corpus-based Set Expansion via Context Feature Selection and Rank Ensemble -- Kernel Methods -- Bayesian Nonlinear Support Vector Machines for Big Data -- Entropic Trace Estimation for Log Determinants -- Fair Kernel Learning -- GaKCo: a Fast Gapped k-mer string Kernel using Counting -- Graph Enhanced Memory Networks for Sentiment Analysis -- Kernel Sequential Monte Carlo -- Learning Lukasiewicz Logic Fragments by Quadratic Programming -- Nystrom sketching -- Learning and Optimization -- Crossprop: learning representations by stochastic meta-gradient descent in neural networks -- Distributed Stochastic Optimization of the Regularized Risk via Saddle-point Problem -- Speeding up Hyper-parameter Optimization by Extrapolation of Learning Curves using Previous Builds -- Matrix and Tensor Factorization -- Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation -- Content-Based Social Recommendation with Poisson Matrix Factorization -- C-SALT: Mining Class-Speci_c ALTerations in Boolean Matrix Factorization -- Feature Extraction for Incomplete Data via Low-rank Tucker Decomposition -- Structurally Regularized Non-negative Tensor Factorization for Spatio-temporal Pattern Discoveries -- Networks and Graphs -- Attributed Graph Clustering with Unimodal Normalized Cut -- K-clique-graphs for Dense Subgraph Discovery -- Learning and Scaling Directed Networks via Graph Embedding -- Local Lanczos Spectral Approximation for Membership Identification -- Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms -- Survival Factorization for Topical Cascades on Diffusion Networks -- The network-untangling problem: From interactions to activity timelines.-TransT: Type-based Multiple Embedding Representations forKnowledge Graph Completion -- Neural Networks and Deep Learning -- A network Architecture for Multi-multi Instance Learning -- CON-S2V: A Generic Framework for Incorporating Extra-Sentential Context into Sen2Vec -- Deep Over-sampling Framework for Classifying Imbalanced Data -- FCNNs: Fourier Convolutional Neural Networks -- Joint User Modeling across Aligned Heterogeneous Sites using Neural Networks -- Sequence Generation with Target Attention -- Wikipedia Vandal Early Detection: from User Behavior to User Embedding. . Tipo de medio : Computadora Summary : The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part I / [documento electrónico] / Ceci, Michelangelo, ; Hollmén, Jaakko, ; Todorovski, LjupÄo, ; Vens, Celine, ; Džeroski, SaÅ¡o, . - 1 ed. . - [s.l.] : Springer, 2017 . - LXIII, 852 p. 245 ilustraciones.
ISBN : 978-3-319-71249-9
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Procesamiento de datos Inteligencia artificial Visión por computador Software de la aplicacion Protección de datos Ordenadores MinerÃa de datos y descubrimiento de conocimientos Aplicaciones informáticas y de sistemas de información Seguridad de datos e información Entornos informáticos Clasificación: 6.312 Resumen: Las actas de tres volúmenes LNAI 10534 – 10536 constituyen las actas arbitradas de la Conferencia Europea sobre Aprendizaje Automático y Descubrimiento de Conocimiento en Bases de Datos, ECML PKDD 2017, celebrada en Skopje, Macedonia, en septiembre de 2017. El total de 101 artÃculos regulares presentados en la parte I y la parte II fue cuidadosamente revisada y seleccionada entre 364 presentaciones; Hay 47 artÃculos 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: detección de anomalÃas; visión por computador; conjuntos y metaaprendizaje; selección y extracción de caracterÃsticas; métodos del núcleo; aprendizaje y optimización, factorización matricial y tensorial; redes y gráficos; Redes neuronales y aprendizaje profundo. Parte II: minerÃa de patrones y secuencias; privacidad y seguridad; modelos y métodos probabilÃsticos; recomendación; regresión; aprendizaje reforzado; descubrimiento de subgrupos; series de tiempo y flujos; transferencia y aprendizaje multitarea; Aprendizaje no supervisado y semisupervisado. Parte III: pista de ciencia de datos aplicada; pista de néctar; y pista de demostración. Nota de contenido: Anomaly Detection -- Concentration Free Outlier Detection -- Efficient top rank optimization with gradient boosting for supervised anomaly detection -- Robust, Deep and Inductive Anomaly Detection -- Sentiment Informed Cyberbullying Detection in Social Media -- zooRank: Ranking Suspicious Activities in Time-Evolving Tensors -- Computer Vision -- Alternative Semantic Representations for Zero-Shot Human Action Recognition -- Early Active Learning with Pairwise Constraint for Person Re-identification -- Guiding InfoGAN with Semi-Supervision -- Scatteract: Automated extraction of data from scatter plots -- Unsupervised Diverse Colorization via Generative Adversarial Networks -- Ensembles and Meta Learning -- Dynamic Ensemble Selection with Probabilistic Classifier Chains -- Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks -- Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks -- Feature Selection and Extraction -- Deep Discrete Hashing with Self-supervised Labels -- Including multi-feature interactions and redundancy for feature ranking in mixed datasets -- Non-redundant Spectral Dimensionality Reduction -- Rethinking Unsupervised Feature Selection: From Pseudo Labels to Pseudo Must-links -- SetExpan: Corpus-based Set Expansion via Context Feature Selection and Rank Ensemble -- Kernel Methods -- Bayesian Nonlinear Support Vector Machines for Big Data -- Entropic Trace Estimation for Log Determinants -- Fair Kernel Learning -- GaKCo: a Fast Gapped k-mer string Kernel using Counting -- Graph Enhanced Memory Networks for Sentiment Analysis -- Kernel Sequential Monte Carlo -- Learning Lukasiewicz Logic Fragments by Quadratic Programming -- Nystrom sketching -- Learning and Optimization -- Crossprop: learning representations by stochastic meta-gradient descent in neural networks -- Distributed Stochastic Optimization of the Regularized Risk via Saddle-point Problem -- Speeding up Hyper-parameter Optimization by Extrapolation of Learning Curves using Previous Builds -- Matrix and Tensor Factorization -- Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation -- Content-Based Social Recommendation with Poisson Matrix Factorization -- C-SALT: Mining Class-Speci_c ALTerations in Boolean Matrix Factorization -- Feature Extraction for Incomplete Data via Low-rank Tucker Decomposition -- Structurally Regularized Non-negative Tensor Factorization for Spatio-temporal Pattern Discoveries -- Networks and Graphs -- Attributed Graph Clustering with Unimodal Normalized Cut -- K-clique-graphs for Dense Subgraph Discovery -- Learning and Scaling Directed Networks via Graph Embedding -- Local Lanczos Spectral Approximation for Membership Identification -- Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms -- Survival Factorization for Topical Cascades on Diffusion Networks -- The network-untangling problem: From interactions to activity timelines.-TransT: Type-based Multiple Embedding Representations forKnowledge Graph Completion -- Neural Networks and Deep Learning -- A network Architecture for Multi-multi Instance Learning -- CON-S2V: A Generic Framework for Incorporating Extra-Sentential Context into Sen2Vec -- Deep Over-sampling Framework for Classifying Imbalanced Data -- FCNNs: Fourier Convolutional Neural Networks -- Joint User Modeling across Aligned Heterogeneous Sites using Neural Networks -- Sequence Generation with Target Attention -- Wikipedia Vandal Early Detection: from User Behavior to User Embedding. . Tipo de medio : Computadora Summary : The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II / Tipo de documento: documento electrónico Autores: Ceci, Michelangelo, ; Hollmén, Jaakko, ; Todorovski, LjupÄo, ; Vens, Celine, ; Džeroski, SaÅ¡o, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XXXIII, 866 p. 213 ilustraciones ISBN/ISSN/DL: 978-3-319-71246-8 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Procesamiento de datos Inteligencia artificial Visión por computador Software de la aplicacion Protección de datos Ordenadores MinerÃa de datos y descubrimiento de conocimientos Aplicaciones informáticas y de sistemas de información Seguridad de datos e información Entornos informáticos Clasificación: 6.312 Resumen: Las actas de tres volúmenes LNAI 10534 – 10536 constituyen las actas arbitradas de la Conferencia Europea sobre Aprendizaje Automático y Descubrimiento de Conocimiento en Bases de Datos, ECML PKDD 2017, celebrada en Skopje, Macedonia, en septiembre de 2017. El total de 101 artÃculos regulares presentados en la parte I y la parte II fue cuidadosamente revisada y seleccionada entre 364 presentaciones; Hay 47 artÃculos 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: detección de anomalÃas; visión por computador; conjuntos y metaaprendizaje; selección y extracción de caracterÃsticas; métodos del núcleo; aprendizaje y optimización, factorización matricial y tensorial; redes y gráficos; Redes neuronales y aprendizaje profundo. Parte II: minerÃa de patrones y secuencias; privacidad y seguridad; modelos y métodos probabilÃsticos; recomendación; regresión; aprendizaje reforzado; descubrimiento de subgrupos; series de tiempo y flujos; transferencia y aprendizaje multitarea; Aprendizaje no supervisado y semisupervisado. Parte III: pista de ciencia de datos aplicada; pista de néctar; y pista de demostración. Nota de contenido: Pattern and Sequence Mining -- BeatLex: Summarizing and Forecasting Time Series with Patterns -- Behavioral Constraint Template-Based Sequence Classification -- Efficient Sequence Regression by Learning Linear Models in All-Subsequence Space -- Subjectively Interesting Connecting Trees -- Privacy and Security -- Malware Detection by Analysing Encrypted Network Traffic with Neural Networks -- PEM: Practical Differentially Private System for Large-Scale Cross-Institutional Data Mining -- Probabilistic Models and Methods -- Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources -- Bayesian Inference for Least Squares Temporal Difference Regularization -- Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints -- Labeled DBN learning with community structure knowledge -- Multi-view Generative Adversarial Networks -- Online Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models -- PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach -- Partial Device Fingerprints -- Robust Multi-view Topic Modeling by Incorporating Detecting Anomalies -- Recommendation -- A Regularization Method with Inference of Trust and Distrust in Recommender Systems -- A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations -- Perceiving the Next Choice with Comprehensive Transaction Embeddings for Online Recommendation -- Regression -- Adaptive Skip-Train Structured Regression for Temporal Networks -- ALADIN: A New Approach for Drug-Target Interaction Prediction -- Co-Regularised Support Vector Regression -- Online Regression with Controlled Label Noise Rate -- Reinforcement Learning -- Generalized Inverse Reinforcement Learning with Linearly Solvable MDP -- Max K-armed bandit: On the ExtremeHunter algorithm and beyond -- Variational Thompson Sampling for Relational Recurrent Bandits -- Subgroup Discovery -- Explaining Deviating Subsetsthrough Explanation Networks -- Flash points: Discovering exceptional pairwise behaviors in vote or rating data -- Time Series and Streams -- A Multiscale Bezier-Representation for Time Series that Supports Elastic Matching -- Arbitrated Ensemble for Time Series Forecasting -- Cost Sensitive Time-series Classification -- Cost-Sensitive Perceptron Decision Trees for Imbalanced Drifting Data Streams -- Efficient Temporal Kernels between Feature Sets for Time Series Classification -- Forecasting and Granger modelling with non-linear dynamical dependencies -- Learning TSK Fuzzy Rules from Data Streams -- Non-Parametric Online AUC Maximization -- On-line Dynamic Time Warping for Streaming Time Series -- PowerCast: Mining and Forecasting Power Grid Sequences -- UAPD: Predicting Urban Anomalies from Spatial-Temporal Data -- Transfer and Multi-Task Learning -- A Novel Rating Pattern Transfer Model for Improving Non-Overlapping Cross-Domain Collaborative Filtering -- Distributed Multi-task Learning for SensorNetwork -- Learning task structure via sparsity grouped multitask learning -- Lifelong Learning with Gaussian Processes -- Personalized Tag Recommendation for Images Using Deep Transfer Learning -- Ranking based Multitask Learning of Scoring Functions -- Theoretical Analysis of Domain Adaptation with Optimal Transport -- TSP: Learning Task-Speci_c Pivots for Unsupervised Domain Adaptation -- Unsupervised and Semisupervised Learning -- k2-means for fast and accurate large scale clustering -- A Simple Exponential Family Framework for Zero-Shot Learning -- DeepCluster: A General Clustering Framework based on Deep Learning -- Multi-view Spectral Clustering on Conflicting Views -- Pivot-based Distributed K-Nearest Neighbor Mining. Tipo de medio : Computadora Summary : The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II / [documento electrónico] / Ceci, Michelangelo, ; Hollmén, Jaakko, ; Todorovski, LjupÄo, ; Vens, Celine, ; Džeroski, SaÅ¡o, . - 1 ed. . - [s.l.] : Springer, 2017 . - XXXIII, 866 p. 213 ilustraciones.
ISBN : 978-3-319-71246-8
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Procesamiento de datos Inteligencia artificial Visión por computador Software de la aplicacion Protección de datos Ordenadores MinerÃa de datos y descubrimiento de conocimientos Aplicaciones informáticas y de sistemas de información Seguridad de datos e información Entornos informáticos Clasificación: 6.312 Resumen: Las actas de tres volúmenes LNAI 10534 – 10536 constituyen las actas arbitradas de la Conferencia Europea sobre Aprendizaje Automático y Descubrimiento de Conocimiento en Bases de Datos, ECML PKDD 2017, celebrada en Skopje, Macedonia, en septiembre de 2017. El total de 101 artÃculos regulares presentados en la parte I y la parte II fue cuidadosamente revisada y seleccionada entre 364 presentaciones; Hay 47 artÃculos 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: detección de anomalÃas; visión por computador; conjuntos y metaaprendizaje; selección y extracción de caracterÃsticas; métodos del núcleo; aprendizaje y optimización, factorización matricial y tensorial; redes y gráficos; Redes neuronales y aprendizaje profundo. Parte II: minerÃa de patrones y secuencias; privacidad y seguridad; modelos y métodos probabilÃsticos; recomendación; regresión; aprendizaje reforzado; descubrimiento de subgrupos; series de tiempo y flujos; transferencia y aprendizaje multitarea; Aprendizaje no supervisado y semisupervisado. Parte III: pista de ciencia de datos aplicada; pista de néctar; y pista de demostración. Nota de contenido: Pattern and Sequence Mining -- BeatLex: Summarizing and Forecasting Time Series with Patterns -- Behavioral Constraint Template-Based Sequence Classification -- Efficient Sequence Regression by Learning Linear Models in All-Subsequence Space -- Subjectively Interesting Connecting Trees -- Privacy and Security -- Malware Detection by Analysing Encrypted Network Traffic with Neural Networks -- PEM: Practical Differentially Private System for Large-Scale Cross-Institutional Data Mining -- Probabilistic Models and Methods -- Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources -- Bayesian Inference for Least Squares Temporal Difference Regularization -- Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints -- Labeled DBN learning with community structure knowledge -- Multi-view Generative Adversarial Networks -- Online Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models -- PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach -- Partial Device Fingerprints -- Robust Multi-view Topic Modeling by Incorporating Detecting Anomalies -- Recommendation -- A Regularization Method with Inference of Trust and Distrust in Recommender Systems -- A Unified Contextual Bandit Framework for Long- and Short-Term Recommendations -- Perceiving the Next Choice with Comprehensive Transaction Embeddings for Online Recommendation -- Regression -- Adaptive Skip-Train Structured Regression for Temporal Networks -- ALADIN: A New Approach for Drug-Target Interaction Prediction -- Co-Regularised Support Vector Regression -- Online Regression with Controlled Label Noise Rate -- Reinforcement Learning -- Generalized Inverse Reinforcement Learning with Linearly Solvable MDP -- Max K-armed bandit: On the ExtremeHunter algorithm and beyond -- Variational Thompson Sampling for Relational Recurrent Bandits -- Subgroup Discovery -- Explaining Deviating Subsetsthrough Explanation Networks -- Flash points: Discovering exceptional pairwise behaviors in vote or rating data -- Time Series and Streams -- A Multiscale Bezier-Representation for Time Series that Supports Elastic Matching -- Arbitrated Ensemble for Time Series Forecasting -- Cost Sensitive Time-series Classification -- Cost-Sensitive Perceptron Decision Trees for Imbalanced Drifting Data Streams -- Efficient Temporal Kernels between Feature Sets for Time Series Classification -- Forecasting and Granger modelling with non-linear dynamical dependencies -- Learning TSK Fuzzy Rules from Data Streams -- Non-Parametric Online AUC Maximization -- On-line Dynamic Time Warping for Streaming Time Series -- PowerCast: Mining and Forecasting Power Grid Sequences -- UAPD: Predicting Urban Anomalies from Spatial-Temporal Data -- Transfer and Multi-Task Learning -- A Novel Rating Pattern Transfer Model for Improving Non-Overlapping Cross-Domain Collaborative Filtering -- Distributed Multi-task Learning for SensorNetwork -- Learning task structure via sparsity grouped multitask learning -- Lifelong Learning with Gaussian Processes -- Personalized Tag Recommendation for Images Using Deep Transfer Learning -- Ranking based Multitask Learning of Scoring Functions -- Theoretical Analysis of Domain Adaptation with Optimal Transport -- TSP: Learning Task-Speci_c Pivots for Unsupervised Domain Adaptation -- Unsupervised and Semisupervised Learning -- k2-means for fast and accurate large scale clustering -- A Simple Exponential Family Framework for Zero-Shot Learning -- DeepCluster: A General Clustering Framework based on Deep Learning -- Multi-view Spectral Clustering on Conflicting Views -- Pivot-based Distributed K-Nearest Neighbor Mining. Tipo de medio : Computadora Summary : The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
