LEADER 03263nam 2200649 450 001 9910816526403321 005 20230803220910.0 010 $a1-59857-680-1 010 $a1-59857-686-0 035 $a(CKB)2550000001197503 035 $a(EBL)1787404 035 $a(SSID)ssj0001114641 035 $a(PQKBManifestationID)12427297 035 $a(PQKBTitleCode)TC0001114641 035 $a(PQKBWorkID)11056124 035 $a(PQKB)11397488 035 $a(MiAaPQ)EBC1787404 035 $a(Au-PeEL)EBL1787404 035 $a(CaPaEBR)ebr10913207 035 $a(OCoLC)868069154 035 $a(EXLCZ)992550000001197503 100 $a20140830h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe principal's handbook for leading inclusive schools /$fby Julie Causton and George Theoharis 210 1$aBaltimore, Maryland :$cPaul H. Brookes Publishing Co.,$d2014. 210 4$dİ2014 215 $a1 online resource (185 p.) 300 $aDescription based upon print version of record. 311 $a1-59857-298-9 320 $aIncludes bibliographical references and index. 327 $aCover; Book Title; Contents; About the Forms; About the Authors; Foreword; Preface; Acknowledgments; Chapter 1:The Principal's Role in Inclusive Schools; Chapter 2:Special Education; Chapter 3:Inclusive Education; Chapter 4:Leading Inclusive School Reform; Chapter 5: The Backbone of Inclusion: LeadingEffective Collaboration; Chapter 6:Rethinking Students: Presuming Competence; Chapter 7:Providing Academic Supports; Appendix Useful Web Sites and Resources for Assistive Technology; Chapter 8:Providing Behavioral Supports; Chapter 9:Supporting You, Supporting Them: Caring for Yourself 327 $aAppendix Self-Care BooksReferences; Index 330 $a Behind every successful inclusive school is a great principal with passion, vision, and a practical plan. With so much riding on their strong and proactive leadership, every principal and administrator will want this how-to book: the essential guide to bringing schoolwide inclusion from theory to practice. Covering everything from the basics of special education to the everyday nuts and bolts of making inclusion work, two renowned inclusion experts give readers clear guidance they can use right away to lead a fully inclusive school where every student learns and belongs. New and veteran admin 606 $aInclusive education$zUnited States 606 $aMainstreaming in education$zUnited States 606 $aSpecial education$zUnited States 606 $aSchool administrators$zUnited States$vHandbooks, manuals, etc 606 $aSchool principals$zUnited States$vHandbooks, manuals, etc 615 0$aInclusive education 615 0$aMainstreaming in education 615 0$aSpecial education 615 0$aSchool administrators 615 0$aSchool principals 676 $a371.9/046 700 $aCauston$b Julie$01628518 702 $aTheoharis$b George 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910816526403321 996 $aThe principal's handbook for leading inclusive schools$94032076 997 $aUNINA LEADER 04195nam 22005295 450 001 9910350230503321 005 20200630013415.0 010 $a981-13-5956-3 024 7 $a10.1007/978-981-13-5956-9 035 $a(CKB)4100000008525793 035 $a(DE-He213)978-981-13-5956-9 035 $a(MiAaPQ)EBC5780041 035 $a(PPN)236520628 035 $a(EXLCZ)994100000008525793 100 $a20190522d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEvolutionary Learning: Advances in Theories and Algorithms /$fby Zhi-Hua Zhou, Yang Yu, Chao Qian 205 $a1st ed. 2019. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2019. 215 $a1 online resource (XII, 361 p. 59 illus., 20 illus. in color.) 311 $a981-13-5955-5 327 $a1.Introduction -- 2. Preliminaries -- 3. Running Time Analysis: Convergence-based Analysis -- 4. Running Time Analysis: Switch Analysis -- 5. Running Time Analysis: Comparison and Unification -- 6. Approximation Analysis: SEIP -- 7. Boundary Problems of EAs -- 8. Recombination -- 9. Representation -- 10. Inaccurate Fitness Evaluation -- 11. Population -- 12. Constrained Optimization -- 13. Selective Ensemble -- 14. Subset Selection -- 15. Subset Selection: k-Submodular Maximization -- 16. Subset Selection: Ratio Minimization -- 17. Subset Selection: Noise -- 18. Subset Selection: Acceleration. . 330 $aMany machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance. . 606 $aArtificial intelligence 606 $aAlgorithms 606 $aComputer science?Mathematics 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aMath Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17044 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aComputer science?Mathematics. 615 14$aArtificial Intelligence. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aMath Applications in Computer Science. 676 $a006.3 700 $aZhou$b Zhi-Hua$4aut$4http://id.loc.gov/vocabulary/relators/aut$0849299 702 $aYu$b Yang$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aQian$b Chao$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910350230503321 996 $aEvolutionary Learning: Advances in Theories and Algorithms$92499269 997 $aUNINA