Machine learning paradigms: applications in recommender systems
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham [u.a.]
Springer
2015
|
| Schriftenreihe: | Intelligent systems reference library
92 |
| Schlagworte: | |
| Online-Zugang: | TUM01 Volltext Inhaltsverzeichnis Abstract |
| Beschreibung: | XV, 125 p. 32 illus., 6 illus. in color |
| ISBN: | 9783319191355 |
| DOI: | 10.1007/978-3-319-19135-5 |
Internformat
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| 245 | 1 | 0 | |a Machine learning paradigms |b applications in recommender systems |c Aristomenis S. Lampropoulos ; George A. Tsihrintzis |
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Datensatz im Suchindex
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| adam_text | MACHINE LEARNING PARADIGMS
/ LAMPROPOULOS, ARISTOMENIS S.
: 2015
TABLE OF CONTENTS / INHALTSVERZEICHNIS
INTRODUCTION
REVIEW OF PREVIOUS WORK RELATED TO RECOMMENDER SYSTEMS
THE LEARNING PROBLEM.-CONTENT DESCRIPTION OF MULTIMEDIA DATA
SIMILARITY MEASURES FOR RECOMMENDATIONS BASED ON OBJECTIVE FEATURE
SUBSET SELECTION
CASCADE RECOMMENDATION METHODS
EVALUATION OF CASCADE RECOMMENDATION METHODS
CONCLUSIONS AND FUTURE WORK
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
MACHINE LEARNING PARADIGMS
/ LAMPROPOULOS, ARISTOMENIS S.
: 2015
ABSTRACT / INHALTSTEXT
THIS TIMELY BOOK PRESENTS APPLICATIONS IN RECOMMENDER SYSTEMS WHICH ARE
MAKING RECOMMENDATIONS USING MACHINE LEARNING ALGORITHMS TRAINED VIA
EXAMPLES OF CONTENT THE USER LIKES OR DISLIKES. RECOMMENDER SYSTEMS
BUILT ON THE ASSUMPTION OF AVAILABILITY OF BOTH POSITIVE AND NEGATIVE
EXAMPLES DO NOT PERFORM WELL WHEN NEGATIVE EXAMPLES ARE RARE. IT IS
EXACTLY THIS PROBLEM THAT THE AUTHORS ADDRESS IN THE MONOGRAPH AT HAND.
SPECIFICALLY, THE BOOKS APPROACH IS BASED ON ONE-CLASS CLASSIFICATION
METHODOLOGIES THAT HAVE BEEN APPEARING IN RECENT MACHINE LEARNING
RESEARCH. THE BLENDING OF RECOMMENDER SYSTEMS AND ONE-CLASS
CLASSIFICATION PROVIDES A NEW VERY FERTILE FIELD FOR RESEARCH,
INNOVATION AND DEVELOPMENT WITH POTENTIAL APPLICATIONS IN “BIG DATA”
AS WELL AS “SPARSE DATA” PROBLEMS. THE BOOK WILL BE USEFUL TO
RESEARCHERS, PRACTITIONERS AND GRADUATE STUDENTS DEALING WITH PROBLEMS
OF EXTENSIVE AND COMPLEX DATA. IT IS INTENDED FOR BOTH THE
EXPERT/RESEARCHER IN THE FIELDS OF PATTERN RECOGNITION, MACHINE LEARNING
AND RECOMMENDER SYSTEMS, AS WELL AS FOR THE GENERAL READER IN THE FIELDS
OF APPLIED AND COMPUTER SCIENCE WHO WISHES TO LEARN MORE ABOUT THE
EMERGING DISCIPLINE OF RECOMMENDER SYSTEMS AND THEIR APPLICATIONS.
FINALLY, THE BOOK PROVIDES AN EXTENDED LIST OF BIBLIOGRAPHIC REFERENCES
WHICH COVERS THE RELEVANT LITERATURE COMPLETELY.
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
| any_adam_object | 1 |
| author | Lampropoulos, Aristomenis S. |
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| building | Verbundindex |
| bvnumber | BV042669493 |
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| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.3 |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Elektrotechnik Maschinenbau |
| doi_str_mv | 10.1007/978-3-319-19135-5 |
| format | Electronic eBook |
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| id | DE-604.BV042669493 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-01T12:06:57Z |
| institution | BVB |
| isbn | 9783319191355 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028101576 |
| oclc_num | 915354108 |
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| physical | XV, 125 p. 32 illus., 6 illus. in color |
| psigel | ZDB-2-ENG ZDB-26-MYL ZDB-2-ENG_2015 ZDB-26-MYL TUM_PDA_MIL_Kauf |
| publishDate | 2015 |
| publishDateSearch | 2015 |
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| publisher | Springer |
| record_format | marc |
| series | Intelligent systems reference library |
| series2 | Intelligent systems reference library |
| spellingShingle | Lampropoulos, Aristomenis S. Machine learning paradigms applications in recommender systems Intelligent systems reference library Engineering Artificial intelligence Computer vision Computational Intelligence Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics Ingenieurwissenschaften Künstliche Intelligenz |
| title | Machine learning paradigms applications in recommender systems |
| title_auth | Machine learning paradigms applications in recommender systems |
| title_exact_search | Machine learning paradigms applications in recommender systems |
| title_full | Machine learning paradigms applications in recommender systems Aristomenis S. Lampropoulos ; George A. Tsihrintzis |
| title_fullStr | Machine learning paradigms applications in recommender systems Aristomenis S. Lampropoulos ; George A. Tsihrintzis |
| title_full_unstemmed | Machine learning paradigms applications in recommender systems Aristomenis S. Lampropoulos ; George A. Tsihrintzis |
| title_short | Machine learning paradigms |
| title_sort | machine learning paradigms applications in recommender systems |
| title_sub | applications in recommender systems |
| topic | Engineering Artificial intelligence Computer vision Computational Intelligence Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics Ingenieurwissenschaften Künstliche Intelligenz |
| topic_facet | Engineering Artificial intelligence Computer vision Computational Intelligence Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics Ingenieurwissenschaften Künstliche Intelligenz |
| url | https://doi.org/10.1007/978-3-319-19135-5 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028101576&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028101576&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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