Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part II
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Leveringstid: Sendes innen 21 dager
Leveringstid: Sendes innen 21 dager
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.
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-Ove