Towards Integrative Machine Learning and Knowledge Extraction : BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers /

Detalles Bibliográficos
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Holzinger, Andreas. (Editor ), Goebel, Randy. (Editor ), Ferri, Massimo. (Editor ), Palade, Vasile. (Editor )
Formato: eBook
Lenguaje:English
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2017.
Edición:1st ed. 2017.
Colección:Lecture Notes in Artificial Intelligence ; 10344
Materias:
Tabla de Contenidos:
  • Towards integrative Machine Learning & Knowledge Extraction
  • Machine Learning and Knowledge Extraction in Digital Pathology needs an integrative approach
  • Comparison of Public-Domain Software and Services for Probabilistic Record Linkage and Address Standardization
  • Better Interpretable Models for Proteomics Data Analysis Using rule-based Mining
  • Probabilistic Logic Programming in Action
  • Persistent topology for natural data analysis — A survey
  • Predictive Models for Differentiation between Normal and Abnormal EEG through Cross-Correlation and Machine Learning Techniques
  • A Brief Philosophical Note on Information
  • Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
  • A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images
  • Topological characteristics of oil and gas reservoirs and their applications
  • Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment.