Support vector machines for predicting distribution of Sudden Oak Death in California

Guo, Q., M. Kelly, and C. Graham. 2005. Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modeling 182(1):75-90

Predicted area of SOD risk in northern CaliforniaWe present an alternative method to conventional environmental niche modeling approaches by developing support vector machines (SVMs), which are the new generation of machine learning algorithms used to find optimal separability between classes within datasets, to predict the potential distribution of Sudden Oak Death in California. Pdf download.

Keywords: Geographic information systems . Support vector machines . Potential disease spread . Sudden Oak Death