03289oam 2200697I 450 991079206540332120230126204241.01-135-06837-20-203-37538-61-299-28017-X1-135-06838-010.4324/9780203375389 (CKB)2560000000099321(EBL)1143804(OCoLC)830161071(SSID)ssj0000833532(PQKBManifestationID)11462181(PQKBTitleCode)TC0000833532(PQKBWorkID)10935601(PQKB)11740677(OCoLC)841648846(MiAaPQ)EBC1143804(Au-PeEL)EBL1143804(CaPaEBR)ebr10672673(CaONFJC)MIL459267(OCoLC)842900551(FINmELB)ELB132920(EXLCZ)99256000000009932120180706d2013 uy 0engur|n|---|||||txtccrLanguage, culture and identity among minority students in China the case of the Hui /Yuxiang WangNew York :Routledge,2013.1 online resource (203 p.)Routledge series on schools and schooling in Asia ;3Description based upon print version of record.0-415-54003-8 Includes bibliographical references and index.Introduction -- Historical context and the Hui in China -- Minority policy and practice in China -- Curricular context -- Theoretical perspectives -- Community, school, and home -- Teacher's perspective: "I treat them as Han students? -- Students' perspective: "we are the same" -- Parents' perspective: "we want them to study the required curriculum as Han students do" -- Conclusion: where shall we go from here?.This book explores Hui (one of the Muslim minority groups in China) students' lived experiences in an elementary school in central P. R. China from the perspectives of philosophical foundations of education and the sociology of education, the impact of their experiences on their identity construction, and what schooling means to Hui students. The book describes a vivid picture of how the Hui construct their own identities in the public school setting, and how the state curricula, teachers, and parents play roles in student identity construction. The objectives of the book are to discover faRoutledge series on schools and schooling in Asia ;3.Hui (Chinese people)EducationMinority studentsChinaNingxia Huizu ZizhiquSocial conditionsHui (Chinese people)Group identityMulticultural educationChinaNingxia Huizu ZizhiquHui (Chinese people)Education.Minority studentsSocial conditions.Hui (Chinese people)Group identity.Multicultural education370.1170951Wang Yuxiang.868851MiAaPQMiAaPQMiAaPQBOOK9910792065403321Language, culture and identity among minority students in China3823841UNINA00968nam a2200241 i 450099100053578970753620020503183732.0990601s1998 it ita b10092067-39ule_instLE02517835ExLFac. Economiaita387.71Tucci, Gianrocco77436Fondamenti economici della de-regulation nei trasporti :elementi teorici ed applicazioni al processo di liberalizzazione del trasporto aereo /Gianrocco TucciRoma :[s.n.],1998104 p. ;25 cmTrasportiAspetti economici.b1009206708-03-1627-06-02991000535789707536LE025 ECO 380 TUC01.0112025000062436le025-E0.00-l- 09390.i1010719827-06-02Fondamenti economici della de-regulation nei trasporti196469UNISALENTOle02501-01-99ma -itait 0105632nam 22007333u 450 991013233490332120240404170240.097811186917861118691784(CKB)3710000000111791(EBL)1687540(FR-PaCSA)88944254(MiAaPQ)EBC1687540(FRCYB88944254)88944254(EXLCZ)99371000000011179120140519d2014|||| u|| |engur|n|---|||||txtrdacontentcrdamediacrrdacarrierBig data, data mining, and machine learning value creation for business leaders and practitioners1st ed.Hoboken :Wiley,20141 online resource (289 pages)Wiley and SAS business seriesTHEi Wiley ebooksDescription based upon print version of record.9781118618042 1118618041 Big Data, Data Mining, and Machine Learning; Contents; Forward; Preface; Acknowledgments; Introduction; Big Data Timeline; Why This Topic Is Relevant Now; Is Big Data a Fad?; Where Using Big Data Makes a Big Difference; Technical Issue; Work Flow Productivity; The Complexities When Data Gets Large; Part One The Computing Environment; Chapter 1 Hardware; Storage (Disk); Central Processing Unit; Graphical Processing Unit; Memory; Network; Chapter 2 Distributed Systems; Database Computing; File System Computing; Considerations; Chapter 3 Analytical Tools; Weka; Java and JVM Languages; R; PythonSASPart Two Turning Data into Business Value; Chapter 4 Predictive Modeling; A Methodology for Building Models; sEMMA; sEMMA for the Big Data Era; Binary Classification; Multilevel Classification; Interval Prediction; Assessment of Predictive Models; Classification; Receiver Operating Characteristic; Lift; Gain; Akaike's Information Criterion; Bayesian Information Criterion; Kolmogorov‐Smirnov; Chapter 5 Common Predictive Modeling Techniques; RFM; Regression; Basic Example of Ordinary Least Squares; Assumptions of Regression Models; Additional Regression TechniquesApplications in the Big Data EraGeneralized Linear Models; Example of a Probit GLM; Applications in the Big Data Era; Neural Networks; Basic Example of Neural Networks; Decision and Regression Trees; Support Vector Machines; Bayesian Methods Network Classification; Naive Bayes Network; Parameter Learning; Learning a Bayesian Network; Inference in Bayesian Networks; Scoring for Supervised Learning; Ensemble Methods; Chapter 6 Segmentation; Cluster Analysis; Distance Measures (Metrics); Evaluating Clustering; Number of Clusters; K-means Algorithm; Hierarchical Clustering; Profiling ClustersChapter 7 Incremental Response ModelingBuilding the Response Model; Measuring the Incremental Response; Chapter 8 Time Series Data Mining; Reducing Dimensionality; Detecting Patterns; Fraud Detection; New Product Forecasting; Time Series Data Mining in Action: Nike+ FuelBand; Seasonal Analysis; Trend Analysis; Similarity Analysis; Chapter 9 Recommendation Systems; What Are Recommendation Systems?; Where Are They Used?; How Do They Work?; Baseline Model; Low‐Rank Matrix Factorization; Stochastic Gradient Descent; Alternating Least Squares; Restricted Boltzmann Machines; Contrastive DivergenceAssessing Recommendation QualityRecommendations in Action: SAS Library; Chapter 10 Text Analytics; Information Retrieval; Content Categorization; Text Mining; Text Analytics in Action: Let's Play Jeopardy!; Information Retrieval Steps; Discovering Topics in Jeopardy! Clues; Topics from Clues Having Incorrect or Missing Answers; Discovering New Topics from Clues; Contestant Analysis: Fantasy Jeopardy!; Part Three Success Stories of Putting It All Together; Chapter 11 Case Study of a Large U.S.-Based Financial Services Company; Traditional Marketing Campaign ProcessHigh-Performance Marketing SolutionWith big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computinWiley and SAS business seriesTHEi Wiley ebooks.Big dataCOMPUTERS / Database Management / Data MiningData miningDatabase managementInformation technology -- ManagementManagement -- Data processingManagementBig data.COMPUTERS / Database Management / Data Mining.Data mining.Database management.Information technology -- Management.Management -- Data processing.Management.658658.05631658/.05631Dean Jared957980AU-PeELAU-PeELAU-PeELBOOK9910132334903321Big data, data mining, and machine learning2170309UNINA