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

• Corporate Author: SpringerLink (Online service)
• Other Authors: Holzinger, Andreas (Editor), Goebel, Randy (Editor), Ferri, Massimo (Editor), Palade, Vasile (Editor)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Series: Lecture Notes in Computer Science, 10344
• Subjects: Computer science.; Computer organization.; Software engineering.; Mathematical statistics.; Computers.; Artificial intelligence.; Computer Science.; Artificial Intelligence (incl. Robotics).; Information Systems and Communication Service.; Probability and Statistics in Computer Science.; Software Engineering/Programming and Operating Systems.; Computer Systems Organization and Communication Networks.

The BIRS Workshop “Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets” (15w2181), held in July 2015 in Banff, Canada, was dedicated to stimulating a cross-domain integrative machine-learning approach and appraisal of “hot topics” toward tackling the grand challe...

Table of Contents:

• 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.