DocumentCode
1947385
Title
Boosting Learning Machines with Function Compositions to Avoid Local Minima in Regression Problems
Author
Zegers, Pablo ; Correa, Gonzalo
Author_Institution
Univ. of the Andes, Santiago
fYear
2007
fDate
12-17 Aug. 2007
Firstpage
1990
Lastpage
1994
Abstract
We present an improved cascaded learning method that is based on mathematical functions compositions, instead of additive models as normally done in boosting approaches. Regression experiments are done to support the usefulness of this architecture and training procedure. The method allows to produce a strong learner with increased probability of avoiding local minima.
Keywords
learning (artificial intelligence); regression analysis; support vector machines; SVM; boosting learning machine; cascaded learning method; mathematical functions composition; probability; regression problem; Boosting; Educational institutions; Guidelines; Learning systems; Least squares methods; Machine learning; Mathematical model; Neural networks; Size measurement; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location
Orlando, FL
ISSN
1098-7576
Print_ISBN
978-1-4244-1379-9
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2007.4371263
Filename
4371263
Link To Document