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
Machine Learning for Text
~
Aggarwal, Charu C.
Machine Learning for Text
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Machine Learning for Text/ by Charu C. Aggarwal.
作者:
Aggarwal, Charu C.
面頁冊數:
XXIII, 493 p. 80 illus., 4 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Data mining. -
電子資源:
https://doi.org/10.1007/978-3-319-73531-3
ISBN:
9783319735313
Machine Learning for Text
Aggarwal, Charu C.
Machine Learning for Text
[electronic resource] /by Charu C. Aggarwal. - 1st ed. 2018. - XXIII, 493 p. 80 illus., 4 illus. in color.online resource.
1 An Introduction to Text Analytics -- 2 Text Preparation and Similarity Computation -- 3 Matrix Factorization and Topic Modeling -- 4 Text Clustering -- 5 Text Classification: Basic Models -- 6 Linear Models for Classification and Regression -- 7 Classifier Performance and Evaluation -- 8 Joint Text Mining with Heterogeneous Data -- 9 Information Retrieval and Search Engines -- 10 Text Sequence Modeling and Deep Learning -- 11 Text Summarization -- 12 Information Extraction -- 13 Opinion Mining and Sentiment Analysis -- 14 Text Segmentation and Event Detection.
Text analytics is a field that lies on the interface of information retrieval, machine learning, and natural language processing. This book carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This book covers text analytics and machine learning topics from the simple to the advanced. Since the coverage is extensive, multiple courses can be offered from the same book, depending on course level.
ISBN: 9783319735313
Standard No.: 10.1007/978-3-319-73531-3doiSubjects--Topical Terms:
528622
Data mining.
LC Class. No.: QA76.9.D343
Dewey Class. No.: 006.312
Machine Learning for Text
LDR
:03238nam a22004095i 4500
001
996099
003
DE-He213
005
20200705065051.0
007
cr nn 008mamaa
008
201225s2018 gw | s |||| 0|eng d
020
$a
9783319735313
$9
978-3-319-73531-3
024
7
$a
10.1007/978-3-319-73531-3
$2
doi
035
$a
978-3-319-73531-3
050
4
$a
QA76.9.D343
072
7
$a
UNF
$2
bicssc
072
7
$a
COM021030
$2
bisacsh
072
7
$a
UNF
$2
thema
072
7
$a
UYQE
$2
thema
082
0 4
$a
006.312
$2
23
100
1
$a
Aggarwal, Charu C.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
681062
245
1 0
$a
Machine Learning for Text
$h
[electronic resource] /
$c
by Charu C. Aggarwal.
250
$a
1st ed. 2018.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2018.
300
$a
XXIII, 493 p. 80 illus., 4 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
1 An Introduction to Text Analytics -- 2 Text Preparation and Similarity Computation -- 3 Matrix Factorization and Topic Modeling -- 4 Text Clustering -- 5 Text Classification: Basic Models -- 6 Linear Models for Classification and Regression -- 7 Classifier Performance and Evaluation -- 8 Joint Text Mining with Heterogeneous Data -- 9 Information Retrieval and Search Engines -- 10 Text Sequence Modeling and Deep Learning -- 11 Text Summarization -- 12 Information Extraction -- 13 Opinion Mining and Sentiment Analysis -- 14 Text Segmentation and Event Detection.
520
$a
Text analytics is a field that lies on the interface of information retrieval, machine learning, and natural language processing. This book carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This book covers text analytics and machine learning topics from the simple to the advanced. Since the coverage is extensive, multiple courses can be offered from the same book, depending on course level.
650
0
$a
Data mining.
$3
528622
650
0
$a
Artificial intelligence.
$3
559380
650
1 4
$a
Data Mining and Knowledge Discovery.
$3
677765
650
2 4
$a
Artificial Intelligence.
$3
646849
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319735306
776
0 8
$i
Printed edition:
$z
9783319735320
776
0 8
$i
Printed edition:
$z
9783030088071
856
4 0
$u
https://doi.org/10.1007/978-3-319-73531-3
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
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