Friedman, J.H., Multivariate Adaptative Regression Splines. Annals of Statistics Vol. 19 1991 Home/Search Document Not in Database Summary Related Articles Check |
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Kernel Regression Trees - Torgo (4 citations) (Correct)
.... approaches to regression include k nearest neighbors (Fix Hodges, 1951) kernel regression (Watson, 1964; Nadaraya, 1964) local regression (Stone, 1977; Cleveland, 1979) radial basis functions, neural networks, projection pursuit regression (Friedman Stuetzle, 1981) adaptive splines (Friedman, 1991), and others. ML researchers have always been mainly concerned with classification problems. The few existent systems dealing with regression usually build axis orthogonal partitions of the input space and fit a parametric model within each of these partitions. These models are usually very simple ....
Friedman, J. (1991) : Multivariate Adaptative Regression Splines. In Annals of Statistics, 19:1.
Regression by Classification - Torgo, Gama (1996) (1 citation) (Correct)
....have done this with their rule based regression system that learns with discrete classes. They have tested it on several domains (including the ones we have used) The results they report show that their system clearly outperforms CART, a Nearest Neighbor algorithm and the statistical method MARS [5]. These results were a key motivation for our work. They indicate that it is possible to obtain good accuracy with classification systems on regression problems. Their system is a two step algorithm. First there is the discretization phase where they use a method that is equal to our VNI KM ....
. Friedman, J. (1991) : Multivariate Adaptative Regression Splines. In Annals of Statistics, 19:1.
Evolutionary Design of Intrusion Detection Programs - Abraham, Grosan, Martin-Vide (2006) (Correct)
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Friedman, J.H., Multivariate Adaptative Regression Splines. Annals of Statistics Vol. 19 1991
Distributed Intrusion Detection Systems: A Computational.. - Ajith Abraham And (2005) (Correct)
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J H Friedman, Multivariate Adaptative Regression Splines. Annals of Statistics Vol. 19 1991
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