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245 1 0 _aTowards Integrative Machine Learning and Knowledge Extraction
_h[electronic resource] :
_bBIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers /
_cedited by Andreas Holzinger, Randy Goebel, Massimo Ferri, Vasile Palade.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXVI, 207 p. 57 illus.
_bonline resource.
336 _atext
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337 _acomputer
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338 _aonline resource
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490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v10344
505 0 _aTowards 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.
520 _aThe 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.
650 0 _aArtificial intelligence.
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650 0 _aComputer networks .
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650 0 _aComputer science
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650 0 _aSoftware engineering.
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650 0 _aComputer engineering.
_910164
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputer Communication Networks.
_990999
650 2 4 _aProbability and Statistics in Computer Science.
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650 2 4 _aSoftware Engineering.
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650 2 4 _aComputer Engineering and Networks.
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700 1 _aHolzinger, Andreas.
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700 1 _aGoebel, Randy.
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700 1 _aFerri, Massimo.
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700 1 _aPalade, Vasile.
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