rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine Learning and Knowledge Discovery in Databases
    sub title: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III
  name:
    name part: Altun, Yasemin.
    role:
      role term: editor.
  name:
    name part: Das, Kamalika.
    role:
      role term: editor.
  name:
    name part: Mielikäinen, Taneli.
    role:
      role term: editor.
  name:
    name part: Malerba, Donato.
    role:
      role term: editor.
  name:
    name part: Stefanowski, Jerzy.
    role:
      role term: editor.
  name:
    name part: Read, Jesse.
    role:
      role term: editor.
  name:
    name part: Žitnik, Marinka.
    role:
      role term: editor.
  name:
    name part: Ceci, Michelangelo.
    role:
      role term: editor.
  name:
    name part: Džeroski, Sašo.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXXV, 448 p. 144 illus. online resource.
  abstract: 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.
  table of contents: 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 Contr…
  note: edited by Yasemin Altun, Kamalika Das, Taneli Mielikäinen, Donato Malerba, Jerzy Stefanowski, Jesse Read, Marinka Žitnik, Michelangelo Ceci, Sašo Džeroski.
  subject:
    topic: Computer science
  subject:
    topic: Computer security
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Image processing
  subject:
    topic: Computers
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Information Systems Applications (incl. Internet)
  subject:
    topic: Systems and Data Security
  subject:
    topic: Computing Milieux
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Computer Science, 10536
  identifier: 9783319712734
  identifier: http://dx.doi.org/10.1007/978-3-319-71273-4
  record info:
    record creation date: 171229
    record change date: 20180116031432.0
    record identifier: 978-3-319-71273-4