Information Technology Reference
In-Depth Information
[50] Sahli, N., Jabeur, N.: Knowledge discovery and reasoning in geospatial applications.
In: Gaber, M.M. (ed.) Scientific data mining and knowledge discovery: Principles and
foundations. Springer, Heidelberg (2010)
[51] Scaringella, N., Zoia, G., Mlynek, D.: Automatic genre classification of music con-
tent: a survey. IEEE Signal Processing Magazine 23, 133-141 (2006)
[52] Schapire, R.E.: The strength of weak learnability. Machine Learning 5(2), 197-227
(1990)
[53] Schapire, R., Singer, Y.: Improved boosting algorithms using confidence-rated pre-
dictions. Machine Learning 37, 297-336 (1999)
[54] Schapire, R., Singer, Y.: BoosTexter: A boosting-based system for text categoriza-
tion. Machine Learning 39(2/3), 135-168 (2000)
[55] Shiraishi, Y., Fukumizu, K.: Statistical approaches to combining binary classifiers for
multi-class classification. Neurocomputing 74, 680-688 (2011)
[56] Singh, M., Curran, E., Cunningham, P.: Active Learning for Multi-Label Image An-
notation. Technical Report UCD-CSI-2009-01, University College Dublin (2009)
[57] Sobol-Shikler, T.: Automatic Inference of Complex Affective States. Computer
Speech and Language 25, 45-62 (2011); doi:10.1016/j.csl.2009.12.005
[58] Sobol-Shikler, T.: Analysis of affective expressions in speech, Tech. report, Universi-
ty of Cambridge (2009)
[59] Sobol-Shikler, T.: Multi-modal analysis of human computer interaction using auto-
matic inference of aural expressions in speech. In: Proc. IEEE International Confe-
rence on Systems, Man & Cybernetics (SMC), Singapore (2008)
[60] Sobol-Shikler, T., Robinson, P.: Classification of complex information: Inference of
co-occurring affective states from their expressions in speech. IEEE Trans. Pattern
Analysis and Machine Intelligence 32(7), 1284-1297 (2010);
doi:10.1109/TPAMI.2009.107
[61] Sowa, J.F.: Knowledge representation. Brokks Cole Publishing, CA (2000)
[62] Tanner, S., Stein, C., Graves, S.J.: On-board data mining. In: Gaber, M.M. (ed.)
Scientific Data Mining and Knowledge Discovery: Principles and Foundations.
Springer, Heidelberg (2010)
[63] Thabtah, F.A., Cowling, P., Peng, Y.: MMAC: A new multi-class, multi-label asso-
ciative classification approach. In: Proc. 4th IEEE International Conference on Data
Mining, ICDM 2004(2004)
[64] Tsoumakas, G., Katakis, I., Vlahavas, I. (20??) Random k-labelsets for multi-label
classification. IEEE Trans. Knowledge and Data Engineering (2010)
[65] Vapnik, V.: Estimation of Dependences Based on Empirical Data. Springer, Heidel-
berg (1982)
[66] Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: Semantics-sensitive integrated match-
ing for picture Libraries. IEEE Trans. Pattern Analysis and Machine Intelli-
gence 23(9), 947-963 (2001)
[67] Wang, H., Huang, M., Wang, X.Z.: A generative probabilistic model for multi-label
classification. In: Proc. 8th IEEE International Conference on Data Mining (2008)
[68] Wang, M., Zhou, X., Chua, T.S.: Automatic Image Annotation via Local Multi-Label
Classification. In: Proc. CIVR 2008, Niagara Falls, Ontario, Canada (2008)
[69] Warrell, J., Prince, S.J.D., Moore, A.P.: Epitomized Priors for Multi-labeling
Problems
[70] Witten, I.H., Frank, E.: Data mining: practical machine learning tools with java im-
plementations. Morgan Kaufmann, San Francisco (2000)
Search WWH ::




Custom Search