CORR
2012
Springer
12 years 9 days ago
2012
Springer
We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input...
ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
Solving linear regression problems based on the total least-squares (TLS) criterion has well-documented merits in various applications, where perturbations appear both in the data...
ICANN
2011
Springer
12 years 8 months ago
2011
Springer
Abstract. This paper presents a neural network tree regression system with dynamic optimization of input variable transformations and post-training optimization. The decision tree ...
CORR
2010
Springer
13 years 3 months ago
2010
Springer
This paper surveys recent work in applying ideas from graphical models and message passing algorithms to solve large scale regularized regression problems. In particular, the focu...
NECO
2006
13 years 4 months ago
2006
The application of boosting technique to the regression problems has received relatively little attention in contrast to the research aimed at classification problems. This paper ...
ML
2006
ACM
13 years 4 months ago
2006
ACM
Abstract This paper proposes a new tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute ...
DMIN
2007
13 years 6 months ago
2007
- This paper investigates a technique for predicting ensemble uncertainty originally proposed in the weather forecasting domain. The overall purpose is to find out if the technique...
ICANN
1997
Springer
13 years 8 months ago
1997
Springer
A new boosting algorithm ADABOOST-R for regression problems is presented and upper bound on the error is obtained. Experimental results to compare ADABOOST-R and other learning alg...
ICDM
2009
IEEE
13 years 11 months ago
2009
IEEE
—Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. ...