05368nam 22007455 450 99646564270331620200703170009.03-319-69775-710.1007/978-3-319-69775-8(CKB)4100000000881991(DE-He213)978-3-319-69775-8(MiAaPQ)EBC6307208(MiAaPQ)EBC5591772(Au-PeEL)EBL5591772(OCoLC)1009042848(PPN)220121605(EXLCZ)99410000000088199120171028d2017 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierTowards Integrative Machine Learning and Knowledge Extraction[electronic resource] BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers /edited by Andreas Holzinger, Randy Goebel, Massimo Ferri, Vasile Palade1st ed. 2017.Cham :Springer International Publishing :Imprint: Springer,2017.1 online resource (XVI, 207 p. 57 illus.)Lecture Notes in Artificial Intelligence ;10344Includes index.3-319-69774-9 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.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.Lecture Notes in Artificial Intelligence ;10344Artificial intelligenceComputersMathematical statisticsSoftware engineeringComputer organizationArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Information Systems and Communication Servicehttps://scigraph.springernature.com/ontologies/product-market-codes/I18008Probability and Statistics in Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/I17036Software Engineering/Programming and Operating Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/I14002Computer Systems Organization and Communication Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/I13006Artificial 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.006.3Holzinger Andreasedthttp://id.loc.gov/vocabulary/relators/edtGoebel Randyedthttp://id.loc.gov/vocabulary/relators/edtFerri Massimoedthttp://id.loc.gov/vocabulary/relators/edtPalade Vasileedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996465642703316Towards Integrative Machine Learning and Knowledge Extraction2829826UNISA