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1. |
Record Nr. |
UNINA9910453557403321 |
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Autore |
Starr Chloë F |
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Titolo |
Red-light novels of the late Qing [[electronic resource] /] / Chloë F. Starr |
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Pubbl/distr/stampa |
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Leiden, : Brill |
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Biggleswade, : Extenza Turpin [distributor], 2007 |
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ISBN |
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1-281-92095-9 |
9786611920951 |
90-474-2859-5 |
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Descrizione fisica |
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1 online resource (319 p.) |
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Collana |
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Disciplina |
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Soggetti |
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Chinese fiction - Qing dynasty, 1644-1912 - History and criticism |
Courtesans in literature |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. [275]-285) and index. |
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Nota di contenuto |
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Preliminary Material / C.F. Starr -- Chapter One. Text And Context / C.F. Starr -- Chapter Two. The Narrator Framed / C.F. Starr -- Chapter Three. Characterisation In Context / C.F. Starr -- Chapter Four. Structure: The Textual Representation Of Itself / C.F. Starr -- Bibliography / C.F. Starr -- Index / C.F. Starr. |
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Sommario/riassunto |
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Chinese literature has traditionally been divided by both theorists and university course providers into ‘classical’ and ‘modern.’ This has left nineteenth-century fiction in limbo, and allowed negative assessments of its quality to persist unchecked. The popularity of Qing dynasty red-light fiction – works whose primary focus is the relationship between clients and courtesans, set in tea-houses, pleasure gardens, and later, brothels – has endured throughout the twentieth century. This volume explores why, arguing that these novels are far from the ‘low’ work of ‘frustrated scholars’ but in their provocative play on the nature of relations between client, courtesan and text, provide an insight into wider changes in understandings of self and literary value in the nineteenth century. |
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2. |
Record Nr. |
UNINA9910790941503321 |
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Autore |
Abbott Dean |
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Titolo |
Applied predictive analytics : principles and techniques for the professional data analyst / / Dean Abbott |
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Pubbl/distr/stampa |
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Indianapolis, Indiana : , : John Wiley & Sons, , 2014 |
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©2014 |
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ISBN |
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1-118-72769-X |
1-118-72793-2 |
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Descrizione fisica |
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1 online resource (453 p.) |
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Disciplina |
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Soggetti |
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Business - Data processing |
Business planning - Data processing |
Business - Computer programs |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Cover; Title Page; Copyright; Contents; Chapter 1 Overview of Predictive Analytics; What Is Analytics?; What Is Predictive Analytics?; Supervised vs. Unsupervised Learning; Parametric vs. Non-Parametric Models; Business Intelligence; Predictive Analytics vs. Business Intelligence; Do Predictive Models Just State the Obvious?; Similarities between Business Intelligence and Predictive Analytics; Predictive Analytics vs. Statistics; Statistics and Analytics; Predictive Analytics and Statistics Contrasted; Predictive Analytics vs. Data Mining; Who Uses Predictive Analytics? |
Challenges in Using Predictive AnalyticsObstacles in Management; Obstacles with Data; Obstacles with Modeling; Obstacles in Deployment; What Educational Background Is Needed to Become a Predictive Modeler?; Chapter 2 Setting Up the Problem; Predictive Analytics Processing Steps: CRISP-DM; Business Understanding; The Three-Legged Stool; Business Objectives; Defining Data for Predictive Modeling; Defining the Columns as Measures; Defining the Unit of Analysis; Which Unit of Analysis?; Defining the Target Variable; Temporal Considerations for Target Variable |
Defining Measures of Success for Predictive ModelsSuccess Criteria for Classification; Success Criteria for Estimation; Other Customized |
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Success Criteria; Doing Predictive Modeling Out of Order; Building Models First; Early Model Deployment; Case Study: Recovering Lapsed Donors; Overview; Business Objectives; Data for the Competition; The Target Variables; Modeling Objectives; Model Selection and Evaluation Criteria; Model Deployment; Case Study: Fraud Detection; Overview; Business Objectives; Data for the Project; The Target Variables; Modeling Objectives |
Model Selection and Evaluation CriteriaModel Deployment; Summary; Chapter 3 Data Understanding; What the Data Looks Like; Single Variable Summaries; Mean; Standard Deviation; The Normal Distribution; Uniform Distribution; Applying Simple Statistics in Data Understanding; Skewness; Kurtosis; Rank-Ordered Statistics; Categorical Variable Assessment; Data Visualization in One Dimension; Histograms; Multiple Variable Summaries; Hidden Value in Variable Interactions: Simpson's Paradox; The Combinatorial Explosion of Interactions; Correlations; Spurious Correlations; Back to Correlations; Crosstabs |
Data Visualization, Two or Higher DimensionsScatterplots; Anscombe's Quartet; Scatterplot Matrices; Overlaying the Target Variable in Summary; Scatterplots in More Than Two Dimensions; The Value of Statistical Significance; Pulling It All Together into a Data Audit; Summary; Chapter 4 Data Preparation; Variable Cleaning; Incorrect Values; Consistency in Data Formats; Outliers; Multidimensional Outliers; Missing Values; Fixing Missing Data; Feature Creation; Simple Variable Transformations; Fixing Skew; Binning Continuous Variables; Numeric Variable Scaling; Nominal Variable Transformation |
Ordinal Variable Transformations |
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Sommario/riassunto |
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Learn the art and science of predictive analytics - techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful p |
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