語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
An Introduction to Pattern Recognition and Machine Learning
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
An Introduction to Pattern Recognition and Machine Learning/ by Paul Fieguth.
作者:
Fieguth, Paul.
面頁冊數:
XXII, 471 p. 270 illus., 265 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Data Mining and Knowledge Discovery. -
電子資源:
https://doi.org/10.1007/978-3-030-95995-1
ISBN:
9783030959951
An Introduction to Pattern Recognition and Machine Learning
Fieguth, Paul.
An Introduction to Pattern Recognition and Machine Learning
[electronic resource] /by Paul Fieguth. - 1st ed. 2022. - XXII, 471 p. 270 illus., 265 illus. in color.online resource.
Chapter 1. Overview -- Chapter 2. Introduction to Pattern Recognition -- Chapter 3. Learning -- Chapter 4. Representing Patterns -- Chapter 5. Feature Extraction and Selection -- Chapter 6. Distance-Based Classification -- Chapter 7. Inferring Class Models -- Chapter 8. Statistics-Based Classification -- Chapter 9. Classifier Testing and Validation -- Chapter 10. Discriminant-Based Classification -- Chapter 11. Ensemble Classification -- Chapter 12. Model-Free Classification -- Chapter 13. Conclusions and Directions.
The domains of Pattern Recognition and Machine Learning have experienced exceptional interest and growth, however the overwhelming number of methods and applications can make the fields seem bewildering. This text offers an accessible and conceptually rich introduction, a solid mathematical development emphasizing simplicity and intuition. Students beginning to explore pattern recognition do not need a suite of mathematically advanced methods or complicated computational libraries to understand and appreciate pattern recognition; rather the fundamental concepts and insights, eminently teachable at the undergraduate level, motivate this text. This book provides methods of analysis that the reader can realistically undertake on their own, supported by real-world examples, case-studies, and worked numerical / computational studies.
ISBN: 9783030959951
Standard No.: 10.1007/978-3-030-95995-1doiSubjects--Topical Terms:
677765
Data Mining and Knowledge Discovery.
LC Class. No.: TK5102.9
Dewey Class. No.: 621.3822
An Introduction to Pattern Recognition and Machine Learning
LDR
:02739nam a22004095i 4500
001
1085313
003
DE-He213
005
20221109064729.0
007
cr nn 008mamaa
008
221228s2022 sz | s |||| 0|eng d
020
$a
9783030959951
$9
978-3-030-95995-1
024
7
$a
10.1007/978-3-030-95995-1
$2
doi
035
$a
978-3-030-95995-1
050
4
$a
TK5102.9
072
7
$a
TJF
$2
bicssc
072
7
$a
UYS
$2
bicssc
072
7
$a
TEC008000
$2
bisacsh
072
7
$a
TJF
$2
thema
072
7
$a
UYS
$2
thema
082
0 4
$a
621.3822
$2
23
100
1
$a
Fieguth, Paul.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
782245
245
1 3
$a
An Introduction to Pattern Recognition and Machine Learning
$h
[electronic resource] /
$c
by Paul Fieguth.
250
$a
1st ed. 2022.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2022.
300
$a
XXII, 471 p. 270 illus., 265 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
505
0
$a
Chapter 1. Overview -- Chapter 2. Introduction to Pattern Recognition -- Chapter 3. Learning -- Chapter 4. Representing Patterns -- Chapter 5. Feature Extraction and Selection -- Chapter 6. Distance-Based Classification -- Chapter 7. Inferring Class Models -- Chapter 8. Statistics-Based Classification -- Chapter 9. Classifier Testing and Validation -- Chapter 10. Discriminant-Based Classification -- Chapter 11. Ensemble Classification -- Chapter 12. Model-Free Classification -- Chapter 13. Conclusions and Directions.
520
$a
The domains of Pattern Recognition and Machine Learning have experienced exceptional interest and growth, however the overwhelming number of methods and applications can make the fields seem bewildering. This text offers an accessible and conceptually rich introduction, a solid mathematical development emphasizing simplicity and intuition. Students beginning to explore pattern recognition do not need a suite of mathematically advanced methods or complicated computational libraries to understand and appreciate pattern recognition; rather the fundamental concepts and insights, eminently teachable at the undergraduate level, motivate this text. This book provides methods of analysis that the reader can realistically undertake on their own, supported by real-world examples, case-studies, and worked numerical / computational studies.
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
677765
650
2 4
$a
Complex Systems.
$3
888664
650
2 4
$a
Automated Pattern Recognition.
$3
1365734
650
1 4
$a
Digital and Analog Signal Processing.
$3
1366690
650
0
$a
Data mining.
$3
528622
650
0
$a
System theory.
$3
566168
650
0
$a
Pattern recognition systems.
$3
557384
650
0
$a
Signal processing.
$3
561459
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783030959937
776
0 8
$i
Printed edition:
$z
9783030959944
856
4 0
$u
https://doi.org/10.1007/978-3-030-95995-1
912
$a
ZDB-2-SMA
912
$a
ZDB-2-SXMS
950
$a
Mathematics and Statistics (SpringerNature-11649)
950
$a
Mathematics and Statistics (R0) (SpringerNature-43713)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入