Information Technology Reference
In-Depth Information
network is more effective than decision tree in case of huge training data set but it
is not convenient for applying classifiers (weight vector W * ) into determining the
classes of documents. Otherwise it is easy to use classification rules taken out
from decision tree for this task.
References
1. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines.
Cambridge University Press, Cambridge (2000)
2. Mitchell, T.: Machine Learning. McGraw-Hill International, New York (1997)
3. Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273-297 (1995)
4. Alrifai, M., Dolog, P., Nejdl, W.: Learner Profile Management for Collaborating
Adaptive eLearning Applications. In: APS 2006: Joint International Workshop on
Adaptivity, Personalization and the Semantic Web at the 17th ACM Hypertext 2006
conference, Odense, Denmark (August 2006)
5. Papatheodorou, C.: Machine Learning in User Modeling. In: Paliouras, G., Karkaletsis,
V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 286-294.
Springer, Heidelberg (2001)
6. Rojas, R.: Neural Networks: A Systematic Introduction. Springer, Berlin (1996)
7. Lippmann, R.P.: An introduction to computing with neural nets. IEEE Transactions on
Acoustics, Speech, and Signal Processing 1987 (1987)
8. Papatheodorou, C.: Machine Learning in User Modeling. In: Paliouras, G., Karkaletsis,
V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 286-294.
Springer, Heidelberg (2001)
9. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Elsevier Inc.,
Amsterdam (2006)
 
Search WWH ::




Custom Search