Machine Learning Paradigms (Record no. 53511)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03497nam a22005055i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-19135-5 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421111156.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 150613s2015 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319191355 |
| -- | 978-3-319-19135-5 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Lampropoulos, Aristomenis S. |
| 245 10 - TITLE STATEMENT | |
| Title | Machine Learning Paradigms |
| Sub Title | Applications in Recommender Systems / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XV, 125 p. 32 illus., 6 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Intelligent Systems Reference Library, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. . |
| 700 1# - AUTHOR 2 | |
| Author 2 | Tsihrintzis, George A. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-319-19135-5 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2015. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer graphics. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence (incl. Robotics). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1868-4394 ; |
| 912 ## - | |
| -- | ZDB-2-ENG |
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