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 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I
  name:
    name part: Berlingerio, Michele.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Bonchi, Francesco.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Gärtner, Thomas.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Hurley, Neil.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Ifrim, Georgiana.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXXVIII, 740 p. 451 illus., 159 illus. in color. online resource.
  abstract: The three volume proceedings LNAI 11051 - 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.
  table of contents: Adversarial Learning -- Image Anomaly Detection with Generative Adversarial Networks -- Image-to-Markup Generation via Paired Adversarial Learning -- Toward an Understanding of Adversarial Examples in Clinical Trials -- ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector -- Anomaly and Outlier Detection -- GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid -- Incorporating Privileged Information to Unsupervised Anomaly Detection -- L1-Depth Revisited: A Robust Angle-based Outlier Factor in High-dimensional Space -- Beyond Outlier Detection: LookOut for Pictorial Explanation -- Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier Features -- Group Anomaly Detection using Deep Generative Models -- Applications -- A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements -- Face-Cap: Image Captioning using Facial Expression Analysis -- Pedestrian Trajectory Prediction with Structured Memory Hierarchies -- Classification -- Multiple Instance Learning with Bag-level Randomized Trees -- One-class Quantification -- Deep F-Measure Maximization in Multi-Label Classification: A Comparative Study -- Ordinal Label Proportions -- AWX: An Integrated Approach to Hierarchical-Multilabel Classification -- Clustering and Unsupervised Learning -- Clustering in the Presence of Concept Drift -- Time Warp Invariant Dictionary Learning for Time Series Clustering -- How Your Supporters and Opponents Define Your Interestingness -- Deep Learning -- Efficient Decentralized Deep Learning by Dynamic Model Averaging -- Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems -- Towards Efficient Forward Propagation on Resource-Constrained Systems -- Auxiliary Guided Autoregressive Variational Autoencoders -- Cooperative Multi-Agent Policy Gradient -- Parametric t-Distributed Stochastic Exemplar-centered Embedding -- Joint autoen…
  note: edited by Michele Berlingerio, Francesco Bonchi, Thomas Gärtner, Neil Hurley, Georgiana Ifrim.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Data mining
  subject:
    topic: Optical data processing
  subject:
    topic: Application software
  subject:
    topic: Computers
  subject:
    topic: Data protection
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Computer Appl. in Social and Behavioral Sciences
  subject:
    topic: Computing Milieux
  subject:
    topic: Security
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Artificial Intelligence ; 11051
  identifier: 9783030109257
  identifier: https://doi.org/10.1007/978-3-030-10925-7
  record info:
    record creation date: 190117
    record change date: 20191220130324.0
    record identifier: 978-3-030-10925-7