03485oam 2200517Ka 450 991026061280332120160803134623.0(CKB)2670000000263681(SSID)ssj0000127633(PQKBManifestationID)11141906(PQKBTitleCode)TC0000127633(PQKBWorkID)10052384(PQKB)10732070(WaSeSS)Ind00065705(OCoLC)827334738(OCoLC-P)827334738(MaCbMITP)2016(PPN)25902158X(EXLCZ)99267000000026368120130212d1997 uy 0engur|||||||||||txtccrComputational learning theory and natural learning systemsCambridge, Mass. ;London MIT Press1 online resource (xxiii, 407 p.)A Bradford BookBibliographic Level Mode of Issuance: Monograph0-262-29113-4 Includes bibliographical references and index.This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and `Natural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI).Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.ContributorsKlaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E. M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S. V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. ZadorComputational learning theoryCongressesCOGNITIVE SCIENCES/GeneralComputational learning theory006.31Greiner Russell1252055Petsche Thomas1252056Hanson Stephen José1142888OCoLC-POCoLC-PBOOK9910260612803321Computational learning theory and natural learning systems2902677UNINA03679nam 22006975 450 991098330170332120250212115240.09783031765940303176594X10.1007/978-3-031-76594-0(CKB)37515816700041(MiAaPQ)EBC31903241(Au-PeEL)EBL31903241(OCoLC)1499718216(DE-He213)978-3-031-76594-0(EXLCZ)993751581670004120250212d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierDesign Tools and Methods in Industrial Engineering IV Proceedings of the Fourth International Conference on Design Tools and Methods in Industrial Engineering, ADM 2024, September 11–13, 2024, Palermo, Italy, Volume 2 /edited by Paolo Di Stefano, Francesco Gherardini, Vincenzo Nigrelli, Caterina Rizzi, Gaetano Sequenzia, Davide Tumino1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (680 pages)Lecture Notes in Mechanical Engineering,2195-43649783031765933 3031765931 This book gathers original peer-reviewed papers reporting on innovative methods and tools in design, modeling, simulation and optimization, and their applications in engineering design, manufacturing, and other relevant industrial sectors. Based on contributions to the Fourth International Conference on Design Tools and Methods in Industrial Engineering, ADM 2024, held on September 11–13, 2024, in Palermo, Italy, and organized by the Italian Association of Design Methods and Tools for Industrial Engineering, and the Department of Engineering of the University of Palermo, this second volume of a 2-volume set focuses on engineering methods in medicine, human factors and ergonomics, and reverse engineering. Further topics include: digital acquisition, image processing and inspection, virtual and augmented reality, virtual prototyping and digital twin, as well as engineering education, and knowledge and product data management. All in all, this book provides academics and professionals with a timely overview and extensive information on trends and technologies in industrial design and manufacturing. .Lecture Notes in Mechanical Engineering,2195-4364Engineering designIndustrial engineeringProduction engineeringUser interfaces (Computer systems)Human-computer interactionEngineering DesignIndustrial and Production EngineeringUser Interfaces and Human Computer InteractionEngineering design.Industrial engineering.Production engineering.User interfaces (Computer systems)Human-computer interaction.Engineering Design.Industrial and Production Engineering.User Interfaces and Human Computer Interaction.620.0042Di Stefano Paolo25699Gherardini Francesco1669365Nigrelli Vincenzo1784712Rizzi Caterina851740Sequenzia Gaetano1784713Tumino Davide1784714MiAaPQMiAaPQMiAaPQBOOK9910983301703321Design Tools and Methods in Industrial Engineering IV4316297UNINA