Gaussian Processes
نویسنده
چکیده
Gaussian processes can be used as a supervised learning technique for classification as well as regression. An example of a classification task would be to recognize handwritten digits, whereas an example of a regression problem would be to learn the inverse dynamics of a robot arm (Fig. 1(a)). For the latter, the task is to obtain a mapping from the state of the arm (given by the positions, velocities and accelerations of the joints) to the corresponding torques on the joints. Such a mapping can then be used to compute the torques needed to move the arm along a given trajectory. Another example is predictive soil mapping (Fig. 1(b)), where one is given a set of soil samples taken from some regions, and asked to predict the nature of soil in another region. A major benefit of using Gaussian processes to solve these problems is that they can provide confidence measures for the predictions. For instance, in the context of predictive soil mapping, one can use Gaussian processes to decide which regions should be given a higher priority for collecting soil samples, based on the uncertainty of the predictions. The following sections provide a mathematical treatment of Gaussian processes, and their application to regression problems.
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تاریخ انتشار 2002