Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017 : proceedings
著者
書誌事項
Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017 : proceedings
（Lecture notes in computer science, 10534-10536 . Lecture notes in artificial intelligence）
Springer, c2017
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- pt. 2
- pt. 3
- タイトル別名
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ECML PKDD 2017
大学図書館所蔵 件 / 全2件
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該当する所蔵館はありません
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注記
Includes bibliographical references and index
"Extras online"-- Cover
内容説明・目次
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pt. 2 ISBN 9783319712451
内容説明
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.
目次
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.
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pt. 1 ISBN 9783319712482
内容説明
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.
目次
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.
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pt. 3 ISBN 9783319712727
内容説明
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.
目次
Applied Data Science track.- A Novel Framework for Online Sales Burst Prediction.- Analyzing Granger causality in climate data with time series classification methods.- Automatic Detection and Recognition of Individuals in Patterned Species.- Boosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting.- CREST - Risk Prediction for Clostridium Difficile Infection Using Multimodal Data Mining.- DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters.- Disjoint-Support Factors and Seasonality Estimation in E-Commerce.- Event Detection and Summarization using Phrase Networks: PhraseNet.- Generalising Random Forest Parameter Optimisation to Include Stability and Cost.- Have It Both Ways - from A/B Testing to A&B Testing with Exceptional Model Mining.- Koopman spectral kernels for comparing complex dynamics: Application to multiagent sport plays.- Modeling the Temporal Nature of Human Behavior for Demographics Prediction.- MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings.- Optimal client recommendation for market makers in illiquid financial products.- Predicting Self-reported Customer Satisfaction of Interactions with a Corporate Call Center.- Probabilistic Inference of Twitter Users' Age based on What They Follow.- Quantifying Heterogeneous Causal Treatment Effects in World Bank Development Finance Projects.- RSSI Based Supervised Learning for Uncooperative Direction-Finding.- Sequential Keystroke Behavioral Biometrics for User Identification via Multi-view Deep Learning.- Session-Based Fraud Detection in Online E-Commerce Transactions Using Recurrent Neural Networks.- SINAS: Suspect Investigation Using Offenders' Activity Space.- Stance Classification of Tweets using Skip Char NGrams.- Structural Semantic Models for Automatic Analysis of Urban Areas.- Taking it for a Test Drive: A Hybrid Spatio-temporal Model for Wildlife Poaching Prediction Evaluated through a Controlled Field Test.- Unsupervised signature extraction from forensic logs.- Urban Water Flow and Water Level Prediction based on Deep Learning.- Using Machine Learning for Labour Market Intelligence.- Nectar track.- Activity-Driven Influence Maximization in Social Networks.- An AI Planning System for Data Cleaning.- Comparing hypotheses on sequential behavior: A Bayesian approach and its applications.- Data-driven Approaches for Smart Parking.- Image representation, annotation and retrieval with predictive clustering trees.- Music Generation Using Bayesian Networks.- Phenotype Inference from Text and Genomic Data.- Process-based Modeling and Design of Dynamical Systems.- QuickScorer: Efficient Traversal of Large Ensembles of Decision Trees.- Recent Advances in Kernel-Based Graph Classification.- Demo track.- ASK-the-Expert: Active learning based knowledge discovery using the expert.- Delve: A Data set Retrieval and Document Analysis System.- Framework for Exploring and Understanding Multivariate Correlations.- Lit@EVE: Explainable Recommendation based on Wikipedia Concept Vectors.- Monitoring Physical Activity and Mental Stress using Wrist-worn Device and a Smartphone.- Tetrahedron: Barycentric Measure Visualizer.- TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting.- TrajViz: A Tool for Visualizing Patterns and Anomalies in Trajectory.- TrAnET: Tracking and Analyzing the Evolution of Topics in Information Networks.- WHODID: Web-based interface for Human-assisted factory Operations in fault Detection, Identification and Diagnosis.
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