An Evaluation of Support Vector Machines as a Pattern Recognition Tool
This work provides an incremental evaluation of SVMs for pattern recognition tasks, relevant for researchers and practitioners in machine learning.
The paper evaluated Support Vector Machines (SVMs) for pattern recognition and object classification, testing various kernels on data from multiple domains to assess generalization performance, but did not report specific numerical results.
The purpose of this report is in examining the generalization performance of Support Vector Machines (SVM) as a tool for pattern recognition and object classification. The work is motivated by the growing popularity of the method that is claimed to guarantee a good generalization performance for the task in hand. The method is implemented in MATLAB. SVMs based on various kernels are tested for classifying data from various domains.