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

Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Holzinger, Andreas. (Editor), Goebel, Randy. (Editor), Ferri, Massimo. (Editor), Palade, Vasile. (Editor)
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2017.
Edition:	1st ed. 2017.
Series:	Lecture Notes in Artificial Intelligence ; 10344
Subjects:	Artificial intelligence. Computers. Mathematical statistics. Software engineering. Computer organization. Artificial Intelligence. Information Systems and Communication Service. Probability and Statistics in Computer Science. Software Engineering/Programming and Operating Systems. Computer Systems Organization and Communication Networks.

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.

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