An Introduction to Machine Learning / (Record no. 21712)

MARC details
000 -LEADER
fixed length control field 02002cam a22003015a 4500
001 - CONTROL NUMBER
control field ssj0001558340
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20201128023735.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 150715t2015 gw a frb 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783319200095
040 ## - CATALOGING SOURCE
Modifying agency WaSeSS
-- EG-ScBUE
Language of cataloging eng
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 22
Item number KUB
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Kubat, Miroslav,
9 (RLIN) 39937
Dates associated with a name 1958-
245 13 - TITLE STATEMENT
Title An Introduction to Machine Learning /
Statement of responsibility, etc Miroslav Kubat.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Cham :
Name of publisher, distributor, etc Springer,
Date of publication, distribution, etc c.2015.
300 ## - PHYSICAL DESCRIPTION
Extent xiii, 291 p. :
Other physical details ill. ;
Dimensions 24 cm.
500 ## - GENERAL NOTE
General note Index : p. 291.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Bibliography : p. 287-290.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note A Simple Machine-Learning Task -- Probabilities: Bayesian Classifiers -- Similarities: Nearest-Neighbor Classifiers -- Inter-Class Boundaries: Linear and Polynomial Classifiers -- Artificial Neural Networks -- Decision Trees -- Computational Learning Theory -- A Few Instructive Applications -- Induction of Voting Assemblies -- Some Practical Aspects to Know About -- Performance Evaluation.-Statistical Significance -- The Genetic Algorithm -- Reinforcement learning.
506 ## - RESTRICTIONS ON ACCESS NOTE
Terms governing access Available on campus and off campus with authorized login.
520 ## - SUMMARY, ETC.
Summary, etc This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting, ” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
Source of heading or term BUEsh
9 (RLIN) 2922
653 ## - INDEX TERM--UNCONTROLLED
Resource For college Informatics and Computer Science
Arrived date list May2016
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783319200095
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Item status Source of classification or shelving scheme Damaged status Not for loan Vendor Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Total Renewals Full call number Barcode Date last seen Date last borrowed Cost, replacement price Koha item type
    Dewey Decimal Classification     Alahram Central Library Central Library Lower Floor 09/05/2016 Purchase 450.00 3 34 006.3 KUB 000032448 15/07/2025 01/10/2018 562.50 Book - Borrowing

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