Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
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Support vector machine is a statistical learning classifier, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. This paper presents a new approach based on support vector machines to predict whether an athlete is aerobically fit or not. The input data set contains physical properties of athletes as well as their cardiopulmonary exercise testing results which were obtained at Cukurova University Sports Physiology Laboratory. According to the exercise test protocol, speed and grade of the treadmill were increased at certain times and the input variables of time, speed and grade of the treadmill, and oxygen uptake, carbon dioxide output, minute ventilation and heart rate of athletes were recorded. The average of the exercise test data was taken over certain time intervals and a curve fitting algorithm was applied to remove the spikes in the data and make it more suitable to use with support vector machines. Experiments with several different training and test data show that curve-fitted data has better performance measures, such as higher prediction rate, sensitivity, specificity, and shorter training time.