Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Road sign classification using Laplace kernel classifier
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Text classification using string kernels
The Journal of Machine Learning Research
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Over the last decade kernel based learning algorithms known as Support Vector Machines (SVMs) have become an attractive tool to solve pattern recognition problems. Choosing an appropriate kernel still is a trial and error approach for SVM however. This research provides some insights into the data characteristics that suit particular kernels. Our approach consists of four main stages. First, the performance of six kernels is examined across a collection of 33 classification problems from the machine learning literature. Secondly, a collection of statistics that describe each of the 33 problems in terms of data complexity is collected. After that, fuzzy C-means (FCM) is used to cluster, and construct a decision tree is used to generate the rules of the 33 problems based on these measurea of complexity. Each cluster represents a group of classification problems with similar data characteristics. The performance of each kernel within each cluster and the rules among the tree is then examined in the final stage to provide both quantitative and qualitative insights into which kernels perform best on certain problem types.