MARC details
| 000 -LEADER |
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| fixed length control field |
02908nam a22002297a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20220828110515.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220828b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783030433864 (pbk) |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
Library of People’s Majlis |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.5 |
| 245 ## - TITLE STATEMENT |
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| Title |
Data science and productivity analytics / |
| Statement of responsibility, etc. |
Vincent Charles, Juan Aparicio, Joe Zhu, editors. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Cham : |
| Name of publisher, distributor, etc. |
Springer, |
| Date of publication, distribution, etc. |
2020. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
x, 439 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
4.5.3 Algorithm 3: Removing the Non-negativity Assumption |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Intro -- Preface -- Contents -- 1 Data Envelopment Analysis and Big Data: Revisit with a Faster Method -- 1.1 Introduction -- 1.2 The Framework -- 1.3 A Basic Example -- 1.3.1 Applying the Framework Without Step 3 -- 1.3.2 Applying the Framework with Step 3 -- 1.4 A Theoretical Example -- 1.4.1 Generating Data -- 1.4.2 The Outcomes of BH -- 1.4.3 The Outcomes of HD -- 1.4.4 Comparison Between BH and the Framework -- 1.5 Uniform and Cobb-Douglas Approaches -- 1.5.1 Generating Data -- 1.5.2 The Outcome -- 1.6 Changing the Cardinality and Dimension -- 1.6.1 Generating Data |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
3.2 Characterising Decision Support Systems (DSS) -- 3.3 From Data Science to Decision-Making -- 3.3.1 Model-Driven and Data-Driven Approaches -- 3.3.2 Descriptive Versus Predictive Models -- 3.4 Principles of Classification Methods -- 3.4.1 The Type of Attributes -- 3.4.2 The First Decision Tree Algorithms -- 3.4.3 Measuring Accuracy (Confusion Matrices) -- 3.4.4 Generating and Reducing Rule Systems -- 3.5 Real Applications of Classification Methods -- 3.5.1 Predicting Customer Behaviour on Vehicle Reservations (Rent-a-Car Company) |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
3.5.2 Extracting Spending Patterns on Tourism (Tourism Valencian Agency) -- 3.5.3 Avoiding Unnecessary Pre-surgery Tests (Healthcare) -- 3.5.4 Classifying Violent/Radicalism on Twitter (Security Surveillance) -- 3.6 From Data Science to DSS in Four Scenarios -- 3.7 Opportunities for Future Research -- References -- 4 Identification of Congestion in DEA -- 4.1 Introduction -- 4.2 Preliminaries -- 4.2.1 Notation -- 4.2.2 The VRS Model -- 4.2.3 Efficiency -- 4.2.4 Finding a Maximal Element of a Non-negative Polyhedral Set -- 4.3 Congestion of Output-Efficient DMUs |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
4.3.1 General Definition of Input Congestion -- 4.3.2 The Congestion Technology -- 4.3.3 Weak and Strong Congestions -- 4.3.4 The Congestion Model -- 4.3.5 The Congestion-Identification Model -- 4.4 Congestion of Output-Inefficient DMUs -- 4.4.1 Congestion of Faces of Technology mathcalTCONG -- 4.4.2 The Minimal Face of an Output-Inefficient DMU -- 4.4.3 A Precise Definition of Congestion for Output-Inefficient DMUs -- 4.5 Three Congestion-Identification Algorithms -- 4.5.1 Algorithm 1: Incorporating the Non-negativity Assumption -- 4.5.2 Algorithm 2: Enhancing Computational Efficiency |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |
