04066nam 22007455 450 991029898200332120250408081211.03-319-13644-510.1007/978-3-319-13644-8(CKB)3710000000372006(EBL)1998202(OCoLC)904397963(SSID)ssj0001465263(PQKBManifestationID)11873793(PQKBTitleCode)TC0001465263(PQKBWorkID)11471537(PQKB)10838805(MiAaPQ)EBC1998202(DE-He213)978-3-319-13644-8(PPN)258861371(PPN)18489042X(EXLCZ)99371000000037200620150303d2014 u| 0engur|n|---|||||txtccrBoosted Statistical Relational Learners From Benchmarks to Data-Driven Medicine /by Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude Shavlik1st ed. 2014.Cham :Springer International Publishing :Imprint: Springer,2014.1 online resource (79 p.)SpringerBriefs in Computer Science,2191-5776Description based upon print version of record.3-319-13643-7 Includes bibliographical references.Introduction -- Statistical Relational Learning -- Boosting (Bi-)Directed Relational Models -- Boosting Undirected Relational Models -- Boosting in the presence of missing data -- Boosting Statistical Relational Learning in Action -- Appendix: Booster System.This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.SpringerBriefs in Computer Science,2191-5776Artificial intelligenceStatisticsData miningMedical informaticsArtificial IntelligenceStatistical Theory and MethodsData Mining and Knowledge DiscoveryHealth InformaticsArtificial intelligence.Statistics.Data mining.Medical informatics.Artificial Intelligence.Statistical Theory and Methods.Data Mining and Knowledge Discovery.Health Informatics.005.75005.756Natarajan Sriraamauthttp://id.loc.gov/vocabulary/relators/aut950756Kersting Kristianauthttp://id.loc.gov/vocabulary/relators/autKhot Tusharauthttp://id.loc.gov/vocabulary/relators/autShavlik Judeauthttp://id.loc.gov/vocabulary/relators/autBOOK9910298982003321Boosted Statistical Relational Learners2149588UNINA