Discovery Science [[electronic resource] ] : 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings / / edited by Sašo Džeroski, Panče Panov, Dragi Kocev, Ljupčo Todorovski |
Edizione | [1st ed. 2014.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
Descrizione fisica | 1 online resource (XXII, 364 p. 111 illus.) |
Disciplina | 006.3 |
Collana | Lecture Notes in Artificial Intelligence |
Soggetto topico |
Artificial intelligence
Data mining Information storage and retrieval Database management Algorithms Artificial Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Database Management Algorithm Analysis and Problem Complexity |
ISBN | 3-319-11812-9 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Explaining Mixture Models through Semantic Pattern Mining and Banded Matrix Visualization -- Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market -- Synthetic Sequence Generator for Recommender Systems – Memory Biased Random Walk on a Sequence Multilayer Network -- Predicting Sepsis Severity from Limited Temporal Observations -- Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining -- Antipattern Discovery in Ethiopian Bagana Songs -- Categorize, Cluster, and Classify: A 3-C Strategy for Scientific Discovery in the Medical Informatics Platform of the Human Brain Project -- Multilayer Clustering: A Discovery Experiment on Country Level Trading Data -- Medical Document Mining Combining Image Exploration and Text Characterization -- Mining Cohesive Itemsets in Graphs -- Mining Rank Data -- Link Prediction on the Semantic MEDLINE Network: An Approach to Literature-Based Discovery -- Medical Image Retrieval Using Multimodal Data -- Fast Computation of the Tree Edit Distance between Unordered Trees Using IP Solvers -- Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency -- Incremental Learning with Social Media Data to Predict Near Real-Time Events -- Stacking Label Features for Learning Multilabel Rules -- Selective Forgetting for Incremental Matrix Factorization in Recommender Systems -- Providing Concise Database Covers Instantly by Recursive Tile Sampling -- Resampling-Based Framework for Estimating Node Centrality of Large Social Network -- Detecting Maximum k-Plex with Iterative Proper l-Plex Search -- Exploiting Bhattacharyya Similarity Measure to Diminish User Cold-Start Problem in Sparse Data -- Failure Prediction – An Application in the Railway Industry -- Wind Power Forecasting Using Time Series Cluster Analysis -- Feature Selection in Hierarchical Feature Spaces -- Incorporating Regime Metrics into Latent Variable Dynamic Models to Detect Early-Warning Signals of Functional Changes in Fisheries Ecology -- An Efficient Algorithm for Enumerating Chordless Cycles and Chordless Paths -- Algorithm Selection on Data Streams -- Sparse Coding for Key Node Selection over Networks -- Variational Dependent Multi-output Gaussian Process Dynamical Systems. |
Record Nr. | UNISA-996199681803316 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. di Salerno | ||
|
Discovery Science : 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings / / edited by Sašo Džeroski, Panče Panov, Dragi Kocev, Ljupčo Todorovski |
Edizione | [1st ed. 2014.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
Descrizione fisica | 1 online resource (XXII, 364 p. 111 illus.) |
Disciplina | 006.3 |
Collana | Lecture Notes in Artificial Intelligence |
Soggetto topico |
Artificial intelligence
Data mining Information storage and retrieval Database management Algorithms Artificial Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Database Management Algorithm Analysis and Problem Complexity |
ISBN | 3-319-11812-9 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Explaining Mixture Models through Semantic Pattern Mining and Banded Matrix Visualization -- Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market -- Synthetic Sequence Generator for Recommender Systems – Memory Biased Random Walk on a Sequence Multilayer Network -- Predicting Sepsis Severity from Limited Temporal Observations -- Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining -- Antipattern Discovery in Ethiopian Bagana Songs -- Categorize, Cluster, and Classify: A 3-C Strategy for Scientific Discovery in the Medical Informatics Platform of the Human Brain Project -- Multilayer Clustering: A Discovery Experiment on Country Level Trading Data -- Medical Document Mining Combining Image Exploration and Text Characterization -- Mining Cohesive Itemsets in Graphs -- Mining Rank Data -- Link Prediction on the Semantic MEDLINE Network: An Approach to Literature-Based Discovery -- Medical Image Retrieval Using Multimodal Data -- Fast Computation of the Tree Edit Distance between Unordered Trees Using IP Solvers -- Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency -- Incremental Learning with Social Media Data to Predict Near Real-Time Events -- Stacking Label Features for Learning Multilabel Rules -- Selective Forgetting for Incremental Matrix Factorization in Recommender Systems -- Providing Concise Database Covers Instantly by Recursive Tile Sampling -- Resampling-Based Framework for Estimating Node Centrality of Large Social Network -- Detecting Maximum k-Plex with Iterative Proper l-Plex Search -- Exploiting Bhattacharyya Similarity Measure to Diminish User Cold-Start Problem in Sparse Data -- Failure Prediction – An Application in the Railway Industry -- Wind Power Forecasting Using Time Series Cluster Analysis -- Feature Selection in Hierarchical Feature Spaces -- Incorporating Regime Metrics into Latent Variable Dynamic Models to Detect Early-Warning Signals of Functional Changes in Fisheries Ecology -- An Efficient Algorithm for Enumerating Chordless Cycles and Chordless Paths -- Algorithm Selection on Data Streams -- Sparse Coding for Key Node Selection over Networks -- Variational Dependent Multi-output Gaussian Process Dynamical Systems. |
Record Nr. | UNINA-9910485145703321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II / / edited by Michelangelo Ceci, Jaakko Hollmén, Ljupčo Todorovski, Celine Vens, Sašo Džeroski |
Edizione | [1st ed. 2017.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
Descrizione fisica | 1 online resource (XXXIII, 866 p. 213 illus.) |
Disciplina | 006.31 |
Collana | Lecture Notes in Artificial Intelligence |
Soggetto topico |
Data mining
Artificial intelligence Optical data processing Application software Computer security Computers Data Mining and Knowledge Discovery Artificial Intelligence Image Processing and Computer Vision Information Systems Applications (incl. Internet) Systems and Data Security Computing Milieux |
ISBN | 3-319-71246-2 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | 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 Subsets through 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 Sensor Network -- 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. |
Record Nr. | UNISA-996465751903316 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. di Salerno | ||
|
Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part I / / edited by Michelangelo Ceci, Jaakko Hollmén, Ljupčo Todorovski, Celine Vens, Sašo Džeroski |
Edizione | [1st ed. 2017.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
Descrizione fisica | 1 online resource (LXIII, 852 p. 245 illus.) |
Disciplina | 006.31 |
Collana | Lecture Notes in Artificial Intelligence |
Soggetto topico |
Data mining
Artificial intelligence Optical data processing Application software Computer security Computers Data Mining and Knowledge Discovery Artificial Intelligence Image Processing and Computer Vision Information Systems Applications (incl. Internet) Systems and Data Security Computing Milieux |
ISBN | 3-319-71249-7 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | 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 for Knowledge 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. . |
Record Nr. | UNISA-996465761003316 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. di Salerno | ||
|
Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II / / edited by Michelangelo Ceci, Jaakko Hollmén, Ljupčo Todorovski, Celine Vens, Sašo Džeroski |
Edizione | [1st ed. 2017.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
Descrizione fisica | 1 online resource (XXXIII, 866 p. 213 illus.) |
Disciplina | 006.31 |
Collana | Lecture Notes in Artificial Intelligence |
Soggetto topico |
Data mining
Artificial intelligence Optical data processing Application software Computer security Computers Data Mining and Knowledge Discovery Artificial Intelligence Image Processing and Computer Vision Information Systems Applications (incl. Internet) Systems and Data Security Computing Milieux |
ISBN | 3-319-71246-2 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | 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 Subsets through 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 Sensor Network -- 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. |
Record Nr. | UNINA-9910484496703321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part I / / edited by Michelangelo Ceci, Jaakko Hollmén, Ljupčo Todorovski, Celine Vens, Sašo Džeroski |
Edizione | [1st ed. 2017.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
Descrizione fisica | 1 online resource (LXIII, 852 p. 245 illus.) |
Disciplina | 006.31 |
Collana | Lecture Notes in Artificial Intelligence |
Soggetto topico |
Data mining
Artificial intelligence Optical data processing Application software Computer security Computers Data Mining and Knowledge Discovery Artificial Intelligence Image Processing and Computer Vision Information Systems Applications (incl. Internet) Systems and Data Security Computing Milieux |
ISBN | 3-319-71249-7 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | 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 for Knowledge 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. . |
Record Nr. | UNINA-9910484496603321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|