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Towards integrative machine learning and knowledge extraction : BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised selected papers / Andreas Holzinger, Randy Goebel, Massimo Ferri, Vasile Palade (eds.).

BIRS Workshop "Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets" (2015 : Banff, Alta.); Holzinger, Andreas, editor.; Goebel, Randy, editor.; Ferri, Massimo, editor.; Palade, Vasile, 1964- editor.
2017
QA76.9.D343
Available Online
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Title
Towards integrative machine learning and knowledge extraction : BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised selected papers / Andreas Holzinger, Randy Goebel, Massimo Ferri, Vasile Palade (eds.).
Meeting Name
BIRS Workshop "Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets" (2015 : Banff, Alta.)
ISBN
9783319697758 (electronic book)
3319697757 (electronic book)
9783319697741
DOI
https://doi.org/10.1007/978-3-319-69775-8
Published
Cham, Switzerland : Springer, 2017.
Language
English
Description
1 online resource (xvi, 207 pages) : illustrations.
Item Number
10.1007/978-3-319-69775-8 doi
Call Number
QA76.9.D343
Dewey Decimal Classification
006.3/12
Summary
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 challenge of reaching a level of useful and useable computational intelligence with a focus on real-world problems, such as in the health domain. This encompasses learning from prior data, extracting and discovering knowledge, generalizing the results, fighting the curse of dimensionality, and ultimately disentangling the underlying explanatory factors in complex data, i.e., to make sense of data within the context of the application domain. The workshop aimed to contribute advancements in promising novel areas such as at the intersection of machine learning and topological data analysis. History has shown that most often the overlapping areas at intersections of seemingly disparate fields are key for the stimulation of new insights and further advances. This is particularly true for the extremely broad field of machine learning.
Note
Includes author index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed November 3, 2017).
Added Author
Holzinger, Andreas, editor.
Goebel, Randy, editor.
Ferri, Massimo, editor.
Palade, Vasile, 1964- editor.
Series
Lecture notes in computer science ; 10344.
Lecture notes in computer science. Lecture notes in artificial intelligence.
LNCS sublibrary. SL 7, Artificial intelligence.
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All Resources
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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