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Incremental Risk Minimization Algorithm Incremental Regression with Polynomials ↑ Incremental (or on-line) learning regression is the process of adapting a model one example at a time without accumulating a batch of data. It has the advantages of allowing continuous adaptation to non-stationary environments, easily handling big data through stream processing, and a fixed low computation and memory demand. The easiest solution is to perform a gradient descent on a squared error metric with each new training example. But this solution does not work well for complex model structures. Especially, the influence of a non-linear transformation of the inputs through a fixed model structure has long been an open problem. During my PhD I worked on an approach which is able to deal with a broad class of non-linear model structures. Its emphasis is on minimizing the effect of local training examples on changes of the global model. Thus, it yields a robust behavior by prevent

United statesUnited kingdomCambridge universityCambridge university pressA library for analysis of onlineJournal of machine learning researchInternational conference on machineInternational conference onIncremental risk minimization algorithmStop auto sampleRisk functionalGradient descentLinear in parametersRisk minimization algorithmOn line machine learningRegression tasks