00913nam a2200241 i 4500991000718469707536041222s1996 us a b 000 0 eng d0844770744b13262531-39ule_instDip.to Filosofiaita331.2153Blau, Francine D.621630Wage inequality :international comparisons of its sources /Francine D. Blau and Lawrence M. KahnWashington, D.C. :AEI Press,1996v, 38 p. :ill. ;22 cmAEI studies on understanding economic inequalityKahn, Lawrence M..b1326253121-09-0622-12-04991000718469707536LE005 55 A 17012005000061554le005-E0.00-l- 00000.i1397283222-12-04Wage inequality3370277UNISALENTOle00522-12-04ma -engus 0003070nam 2200505 450 991031779010332120221013110358.01-83881-418-31-78923-753-X(CKB)4970000000100183(NjHacI)994970000000100183(oapen)https://directory.doabooks.org/handle/20.500.12854/52515(MiAaPQ)EBC30390193(Au-PeEL)EBL30390193(EXLCZ)99497000000010018320221013d2018 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierMachine Learning Advanced Techniques and Emerging Applications /edited by Hamed Farhadi1st ed.IntechOpen2018London, England :IntechOpen,2018.1 online resource (230 pages)1-78923-752-1 The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.Machine learning Machine learningPhysical SciencesEngineering and TechnologyComputer and Information ScienceArtificial IntelligenceMachine LearningMachine learning.006.31Hamed Farhadiauth1364600Farhadi HamedNjHacINjHaclBOOK9910317790103321Machine Learning3386030UNINA03617nam 22007695 450 991029970120332120251113182106.09783319160009331916000110.1007/978-3-319-16000-9(CKB)3710000000360336(EBL)1998048(OCoLC)904131861(SSID)ssj0001452113(PQKBManifestationID)11889959(PQKBTitleCode)TC0001452113(PQKBWorkID)11498454(PQKB)10813875(DE-He213)978-3-319-16000-9(MiAaPQ)EBC1998048(PPN)184499143(EXLCZ)99371000000036033620150225d2015 u| 0engur|n|---|||||txtccrProbability Collectives A Distributed Multi-agent System Approach for Optimization /by Anand Jayant Kulkarni, Kang Tai, Ajith Abraham1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (162 p.)Intelligent Systems Reference Library,1868-4408 ;86Description based upon print version of record.9783319159997 3319159992 Includes bibliographical references at the end of each chapters.Introduction to Optimization -- Probability Collectives: A Distributed Optimization Approach -- Constrained Probability Collectives: A Heuristic Approach -- Constrained Probability Collectives with a Penalty Function Approach -- Constrained Probability Collectives With Feasibility-Based Rule I -- Probability Collectives for Discrete and Mixed Variable Problems -- Probability Collectives with Feasibility-Based Rule II.This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.Intelligent Systems Reference Library,1868-4408 ;86Computational intelligenceArtificial intelligenceSystem theoryMathematical physicsComputational IntelligenceArtificial IntelligenceComplex SystemsTheoretical, Mathematical and Computational PhysicsComputational intelligence.Artificial intelligence.System theory.Mathematical physics.Computational Intelligence.Artificial Intelligence.Complex Systems.Theoretical, Mathematical and Computational Physics.006.3Kulkarni Anand Jayantauthttp://id.loc.gov/vocabulary/relators/aut720592Tai Kangauthttp://id.loc.gov/vocabulary/relators/autAbraham Ajith1968-authttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910299701203321Probability Collectives2507771UNINA