TK Ho - IEEE transactions on pattern analysis and machine …, 1998 - ieeexplore.ieee.org
Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom …
JH Friedman, BE Popescu - 2008 - projecteuclid.org
General regression and classification models are constructed as linear combinations of simple rules derived from the data. Each rule consists of a conjunction of a small number of …
The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression …
RE Fan, PH Chen, CJ Lin, T Joachims - Journal of machine learning …, 2005 - jmlr.org
Working set selection is an important step in decomposition methods for training support vector machines (SVMs). This paper develops a new technique for working set selection in …
YJ Lee, OL Mangasarian - Proceedings of the 2001 SIAM international …, 2001 - SIAM
An algorithm is proposed which generates a nonlinear kernel-based separating surface that requires as little as 1% of a large dataset for its explicit evaluation. To generate this …
CW Hsu, CJ Lin - Machine Learning, 2002 - Springer
The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper …
TK Ho - Advances in Pattern Recognition: Joint IAPR …, 1998 - Springer
Recent studies have shown that the random subspace method can be used to create multiple independent tree-classifiers that can be combined to improve accuracy. We apply …
EM Kleinberg - The annals of statistics, 1996 - projecteuclid.org
We will introduce a generic approach for solving problems in pattern recognition based on the synthesis of accurate multiclass discriminators from large numbers of very inaccurate" …
Kernel trick is widely applied to Support Vector Machine (SVM) to deal with linearly inseparable data which is known as kernel SVM. However, kernel SVM always has high …