This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Learning Nonlinear Functions Using Regularized Greedy Forest
PrePrint
ISSN: 0162-8828
Rie Johnson, RJ Research Consulting, Tarrytown
Tong Zhang, Rutgers University
We consider the problem of learning a forest of nonlinear decision rules with general loss functions. The standard methods employ boosted decision trees such as Adaboost for exponential loss and Friedman's gradient boosting for general loss. In contrast to these traditional boosting algorithms that treat a tree learner as a black box, the method we propose directly learns decision forests via fully-corrective regularized greedy search using the underlying forest structure. Our method achieves higher accuracy and smaller models than gradient boosting on many of the datasets we have tested on.
Index Terms:
Boosting,Decision trees,Vegetation,Additives,Tuning,Greedy algorithms,Vectors,greedy algorithm,boosting,decision tree,decision forest,ensemble
Citation:
Rie Johnson, Tong Zhang, "Learning Nonlinear Functions Using Regularized Greedy Forest," IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 Nov. 2013. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.159>
Usage of this product signifies your acceptance of the Terms of Use.