MARC details
| 000 -LEADER |
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| fixed length control field |
02037nam a22002297a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20220204103738.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220204b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783030088071 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
AGG |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Aggarwal, Charu C. |
| 245 ## - TITLE STATEMENT |
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| Title |
Machine learning for text |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc. |
Springer |
| Place of publication, distribution, etc. |
2018 |
| Date of publication, distribution, etc. |
Switzerland |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxiii, 493 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
EURO |
| Price amount |
49.99 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Introduction<br/>Text analytics is a field that lies on the interface of information retrieval, machine learning,<br/><br/>and natural language processing. This book carefully covers a coherently organized framework<br/><br/>drawn from these intersecting topics. The chapters of this book span three broad categories:<br/><br/> <br/><br/>1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics<br/><br/>such as preprocessing, similarity computation, topic modeling, matrix factorization,<br/><br/>clustering, classification, regression, and ensemble analysis.<br/><br/> <br/><br/>2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous<br/><br/>settings such as a combination of text with multimedia or Web links. The problem of<br/><br/>information retrieval and Web search is also discussed in the context of its relationship<br/><br/>with ranking and machine learning methods.<br/><br/> <br/><br/>3. Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and<br/><br/>natural language applications, such as feature engineering, neural language models,<br/><br/>deep learning, text summarization, information extraction, opinion mining, text segmentation,<br/><br/>and event detection.<br/><br/> <br/><br/>This book covers text analytics and machine learning topics from the simple to the advanced.<br/><br/>Since the coverage is extensive, multiple courses can be offered from the same book,<br/><br/>depending on course level. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Text processing (Computer science) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Artificial intelligence |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Computer science |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data mining |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |