10,503 research outputs found
A CASE STUDY ON SUPPORT VECTOR MACHINES VERSUS ARTIFICIAL NEURAL NETWORKS
The capability of artificial neural networks for pattern recognition of real world problems is well known. In recent years, the support vector machine has been advocated for its structure risk minimization leading to tolerance margins of decision boundaries. Structures and performances of these pattern classifiers depend on the feature dimension and training data size. The objective of this research is to compare these pattern recognition systems based on a case study. The particular case considered is on classification of hypertensive and normotensive right ventricle (RV) shapes obtained from Magnetic Resonance Image (MRI) sequences. In this case, the feature dimension is reasonable, but the available training data set is small, however, the decision surface is highly nonlinear.For diagnosis of congenital heart defects, especially those associated with pressure and volume overload problems, a reliable pattern classifier for determining right ventricle function is needed. RV¡¦s global and regional surface to volume ratios are assessed from an individual¡¦s MRI heart images. These are used as features for pattern classifiers. We considered first two linear classification methods: the Fisher linear discriminant and the linear classifier trained by the Ho-Kayshap algorithm. When the data are not linearly separable, artificial neural networks with back-propagation training and radial basis function networks were then considered, providing nonlinear decision surfaces. Thirdly, a support vector machine was trained which gives tolerance margins on both sides of the decision surface. We have found in this case study that the back-propagation training of an artificial neural network depends heavily on the selection of initial weights, even though randomized. The support vector machine where radial basis function kernels are used is easily trained and provides decision tolerance margins, in spite of only small margins
A System for Induction of Oblique Decision Trees
This article describes a new system for induction of oblique decision trees.
This system, OC1, combines deterministic hill-climbing with two forms of
randomization to find a good oblique split (in the form of a hyperplane) at
each node of a decision tree. Oblique decision tree methods are tuned
especially for domains in which the attributes are numeric, although they can
be adapted to symbolic or mixed symbolic/numeric attributes. We present
extensive empirical studies, using both real and artificial data, that analyze
OC1's ability to construct oblique trees that are smaller and more accurate
than their axis-parallel counterparts. We also examine the benefits of
randomization for the construction of oblique decision trees.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
Making Indefinite Kernel Learning Practical
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and see why this paradigm is successful for many pattern recognition problems. We then embed evolutionary computation into the most prominent representative of this class of learning methods, namely into Support Vector Machines (SVM). In contrast to former applications of evolutionary algorithms to SVM we do not only optimize the method or kernel parameters. We rather use evolution strategies in order to directly solve the posed constrained optimization problem. Transforming the problem into the Wolfe dual reduces the total runtime and allows the usage of kernel functions just as for traditional SVM. We will show that evolutionary SVM are at least as accurate as their quadratic programming counterparts on eight real-world benchmark data sets in terms of generalization performance. They always outperform traditional approaches in terms of the original optimization problem. Additionally, the proposed algorithm is more generic than existing traditional solutions since it will also work for non-positive semidefinite or indefinite kernel functions. The evolutionary SVM variants frequently outperform their quadratic programming competitors in cases where such an indefinite Kernel function is used. --
Elastic-Net Regularization in Learning Theory
Within the framework of statistical learning theory we analyze in detail the
so-called elastic-net regularization scheme proposed by Zou and Hastie for the
selection of groups of correlated variables. To investigate on the statistical
properties of this scheme and in particular on its consistency properties, we
set up a suitable mathematical framework. Our setting is random-design
regression where we allow the response variable to be vector-valued and we
consider prediction functions which are linear combination of elements ({\em
features}) in an infinite-dimensional dictionary. Under the assumption that the
regression function admits a sparse representation on the dictionary, we prove
that there exists a particular ``{\em elastic-net representation}'' of the
regression function such that, if the number of data increases, the elastic-net
estimator is consistent not only for prediction but also for variable/feature
selection. Our results include finite-sample bounds and an adaptive scheme to
select the regularization parameter. Moreover, using convex analysis tools, we
derive an iterative thresholding algorithm for computing the elastic-net
solution which is different from the optimization procedure originally proposed
by Zou and HastieComment: 32 pages, 3 figure
- …