Handbook of big data (Record no. 205219)

000 -LEADER
fixed length control field 04379cam a22002537i 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 160614t20162016flua bf 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781482249071
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.7
Item number H2
245 00 - TITLE STATEMENT
Title Handbook of big data
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Boca Raton
Name of publisher, distributor, etc CRC Press
Date of publication, distribution, etc 2016
300 ## - PHYSICAL DESCRIPTION
Extent xvi, 464 p.
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Chapman & Hall/CRC handbooks of modern statistical methods
9 (RLIN) 321791
500 ## - GENERAL NOTE
General note "Chapman & Hall book."
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Table of Contents


1. GENERAL PERSPECTIVES ON BIG DATA
The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data
Richard Starmans
Big n versus Big p in Big Data
Norman Matloff

2. DATA-CENTRIC, EXPLORATORY METHODS
Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data
Ryan Hafen
Integrate Big Data for Better Operation, Control, and Protection of Power Systems
Guang Lin
Interactive Visual Analysis of Big Data
Carlos Scheidegger
A Visualization Tool for Mining Large Correlation Tables: The Association Navigator
Andreas Buja, Abba M. Krieger, and Edward I. George

3. EFFICIENT ALGORITHMS
High-Dimensional Computational Geometry
Alexandr Andoni
IRLBA: Fast Partial SVD Method
James Baglama
Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms
Michael W. Mahoney and Petros Drineas
Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms
Ronitt Rubinfeld and Eric Blais

4. GRAPH APPROACHES
Networks
Elizabeth L. Ogburn and Alexander Volfovsky
Mining Large Graphs
David F. Gleich and Michael W. Mahoney

5. MODEL FITTING AND REGULARIZATION
Estimator and Model Selection Using Cross-Validation
Iván Díaz
Stochastic Gradient Methods for Principled Estimation with Large Datasets
Panos Toulis and Edoardo M. Airoldi
Learning Structured Distributions
Ilias Diakonikolas
Penalized Estimation in Complex Models
Jacob Bien and Daniela Witten
High-Dimensional Regression and Inference
Lukas Meier

6. ENSEMBLE METHODS
Divide and Recombine Subsemble, Exploiting the Power of Cross-Validation
Stephanie Sapp and Erin LeDell
Scalable Super Learning
Erin LeDell

7. CAUSAL INFERENCE
Tutorial for Causal Inference
Laura Balzer, Maya Petersen, and Mark van der Laan
A Review of Some Recent Advances in Causal Inference
Marloes H. Maathuis and Preetam Nandy

8. TARGETED LEARNING
Targeted Learning for Variable Importance
Sherri Rose
Online Estimation of the Average Treatment Effect
Sam Lendle
Mining with Inference: Data-Adaptive Target Parameters
Alan Hubbard and Mark van der Laan



520 ## - SUMMARY, ETC.
Summary, etc Features

Depicts the current landscape of big data analysis
Emphasizes computational statistics and machine learning
Strikes the right balance not only between statistical theory and applications in computer science but also between the breadth of topics and the depth to which each topic is explored

Summary

Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice.

Offering balanced coverage of methodology, theory, and applications, this handbook:

Describes modern, scalable approaches for analyzing increasingly large datasets
Defines the underlying concepts of the available analytical tools and techniques
Details intercommunity advances in computational statistics and machine learning

Handbook of Big Data also identifies areas in need of further development, encouraging greater communication and collaboration between researchers in big data sub-specialties such as genomics, computational biology, and finance.


https://www.crcpress.com/Handbook-of-Big-Data/Buhlmann-Drineas-Kane-van-der-Laan/p/book/9781482249071
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big data
9 (RLIN) 338776
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistical methods
9 (RLIN) 18522
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Handbooks - Manuals
9 (RLIN) 29873
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Buhlmann, Peter
Relator term Editor
9 (RLIN) 338777
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Drineas, Petros
Relator term Editor
9 (RLIN) 338778
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Kane, Michael
Relator term Editor
9 (RLIN) 338779
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Laan, M. J. van der
Relator term Editor
9 (RLIN) 338780
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Source of acquisition Cost, normal purchase price Item location Total Checkouts Total Renewals Full call number Barcode Checked out Date last seen Date last borrowed Cost, replacement price Koha item type
          Non-fiction Vikram Sarabhai Library Vikram Sarabhai Library   2016-12-26 5 5831.84 Slot 83 (0 Floor, West Wing) 2 5 005.7 H2 193431 2019-12-28 2019-01-04 2019-01-04 7289.80 Books

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