A Divide and Conquer Approach to Learning from Prior Knowledge
This paper introduces a new machine learning task|model calibration|and presents a method for solving a particularly diicult model calibration task that arose as part of a global climate change research project. The model calibration task is the problem of training the free parameters of a scientiic model in order to optimize the accuracy of the model for making future predictions. It is a form of supervised learning from examples in the presence of prior knowledge. An obvious approach to solving calibration problems is to formulate them as global optimization problems in which the goal is to nd values for the free parameters that minimize the error of the model on training data. Unfortunately, this global optimization approach becomes computationally infeasible when the model is highly nonlinear. This paper presents a new divide-and-conquer method that analyzes the model to identify a series of smaller optimization problems whose sequential solution solves the global calibration problem. This paper argues that methods of this kind|rather than global optimization techniques|will be required in order for agents with large amounts of prior knowledge to learn eeciently.