TY  - BOOK
AU  - Holzinger,Andreas
AU  - Goebel,Randy
AU  - Ferri,Massimo
AU  - Palade,Vasile
ED  - SpringerLink (Online service)
TI  - Towards Integrative Machine Learning and Knowledge Extraction: BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers 
T2  - Lecture Notes in Artificial Intelligence
SN  - 9783319697758
AV  - Q334-342 
U1  - 006.3 23
PY  - 2017///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Artificial intelligence
KW  - Computers
KW  - Mathematical statistics
KW  - Software engineering
KW  - Computer organization
KW  - Artificial Intelligence
KW  - Information Systems and Communication Service
KW  - Probability and Statistics in Computer Science
KW  - Software Engineering/Programming and Operating Systems
KW  - Computer Systems Organization and Communication Networks
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-3-319-69775-8
ER  -