Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II

AvMichelangelo Ceci,Jaakko Hollmén

Häftad, Engelska, 2018

Del 10535 i serien Lecture Notes in Computer Science

558 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2018-01-10
Mått:155 x 235 x 48 mm
Vikt:1 334 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:866
Förlag:Springer International Publishing AG
ISBN:9783319712451

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT

Innehållsförteckning

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 RecommenderSystems.- 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.