Gubachelier am 19. Juni 2016 um 12:43 Uhr

2016-06-19T12:43:40Z

Gubachelier: Die Seite wurde neu angelegt: „ == Referenz == N. Panda, K. Goh, and E. Y. Chang: Active learning in very large databases. MTAP, 31(3):249-267, 2006. == DOI == http://dx.doi.org/10.1…“

2016-06-19T12:42:54Z

Die Seite wurde neu angelegt: „ == Referenz == N. Panda, K. Goh, and E. Y. Chang: Active learning in very large databases. MTAP, 31(3):249-267, 2006. == DOI == http://dx.doi.org/10.1…“

Neue Seite

== Referenz ==
N. Panda, K. Goh, and E. Y. Chang: [[Active learning in very large databases]]. MTAP, 31(3):249-267, 2006.

== DOI ==
http://dx.doi.org/10.1007/s11042-006-0043-1

== AbstractQuery-by-example and query-by-keyword both suffer from the problem of “aliasing,” meaning that example-images and keywords potentially have variable interpretations or multiple semantics. For discerning which semantic is appropriate for a given query, we have established that combining active learning with kernel methods is a very effective approach. In this work, we first examine active-learning strategies, and then focus on addressing the challenges of two scalability issues: scalability in concept complexity and in dataset size. We present remedies, explain limitations, and discuss future directions that research might take.

== Extended Abstract ==

== Bibtex ==
@Article{Panda2006,
author="Panda, Navneet and Goh, King-Shy and Chang, Edward Y.",
title="Active learning in very large databases",
journal="Multimedia Tools and Applications",
year="2006",
volume="31",
number="3",
pages="249--267",
issn="1573-7721",
doi="10.1007/s11042-006-0043-1",
url="http://dx.doi.org/10.1007/s11042-006-0043-1 http://de.evo-art.org/index.php?title=Active_learning_in_very_large_databases"
}

== Used References ==
1. Blum A, Mitchell T (1998) Combining labeled and unlabeled data wih co-training. In: Proceedings of the workshop on computational learning theory, Madison, Wisconsin, 92–100

2. Brinker K (2003) Incorporating diversity in active learning with support vector machines. In: Prooceedings of the twentieth international conference on machine learning, Washington, District of Columbia, 59–66

3. Chang E, Goh K, Sychay G, Wu G (2003a) CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines. IEEE Trans Circuits Syst Video Technol 13(1):26–38 (Special issue on conceptual and dynamic aspects of multimedia content description)CrossRef

4. Chang E, Li B (2003) MEGA—the maximizing expected generalization algorithm for learning complex query concepts. ACM Trans Inf. Sys. 21(4):347–382 http://dx.doi.org/10.1145/944012.944014

5. Chang E, Li B, Wu G, Goh K-S (2003b) Statistical learning for effective visual information retrieval. In: IEEE Conference in Image Processing, Barcelona, Spain, 606–612

6. Flickner M, Sawhney H, Ashley J, Huang Q, Dom B, Gorkani M, Hafner J, Lee D, Petkovic D, Steele D, Yanker P (1995) Query by image and video content: the QBIC system. IEEE Computer 28(9):23–32

7. Goh K, Chang EY, Lai W-C (2004) Concept-dependent multimodal active learning for image retrieval. In: ACM international conference on multimedia, New York, New York, 564–571

8. Li B, Chang, E (2003) Discovery of a perceptual distance function for measuring image similarity. ACM Multimedia J. 8(6):512–522 (Special issue on content-based image retrieval) http://dx.doi.org/10.1007/s00530-002-0069-9

9. Li C, Chang E, Garcia-Molina H, Wiederhold G (2002) Clustering for approximate similarity queries in high-dimensional spaces. IEEE Trans Knowl Data Eng. 14(4):792–808 http://dx.doi.org/10.1109/TKDE.2002.1019208

10. Panda N, Chang E (2005) Exploiting geometry for support vector machine indexing. In: SIAM conference on data mining, Newport Beach, California

11. Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: Proceedings of ACM international conference on multimedia, Ottawa, Canada, 107–118

12. Tong S, Koller D (2000) Support vector machine active learning with applications to text classification. In: Proceedings of the 17th international conference on machine learning, Stanford, USA, 401–412

13. Vapnik V (1995) The nature of statistical learning theory. Springer, Berlin Heidelberg New York

14. Zhang Z, Wu G, Wang G, Chang E (2005) Bayesian kernel regression. In: International conference on machine learning, Bonn, Germany

== Links ==

=== Full Text ===
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.76.5446&rep=rep1&type=pdf

[[internal file]]

=== Sonstige Links ===

@@ Zeile 7: / Zeile 7: @@
 http://dx.doi.org/10.1007/s11042-006-0043-1
-== AbstractQuery-by-example and query-by-keyword both suffer from the problem of “aliasing,” meaning that example-images and keywords potentially have variable interpretations or multiple semantics. For discerning which semantic is appropriate for a given query, we have established that combining active learning with kernel methods is a very effective approach. In this work, we first examine active-learning strategies, and then focus on addressing the challenges of two scalability issues: scalability in concept complexity and in dataset size. We present remedies, explain limitations, and discuss future directions that research might take.
+== Abstract ==
 == Extended Abstract ==

Active learning in very large databases - Versionsgeschichte

Gubachelier am 19. Juni 2016 um 12:43 Uhr

Gubachelier: Die Seite wurde neu angelegt: „ == Referenz == N. Panda, K. Goh, and E. Y. Chang: Active learning in very large databases. MTAP, 31(3):249-267, 2006. == DOI == http://dx.doi.org/10.1…“