9504054 Poggio The research project consists of three parts. First, the investigator plans to sharpen the tools to analyze the sample complexity question -- how many examples does the learner need to generalize well? This is related to the complexity of the model the learner is using to fit the data and generalize to unseen data. Implications for model selection and data mining will be explored. Second, it is proposed to develop active algorithms which choose their own examples. This reduces the sample complexity of learning at the cost of an increased computational burden. Access to high performance computing will help greatly. Applications to function approximation, pattern classification and system identification will be explored. Finally, the tools developed earlier will be applied to the domain of natural languages. In particular, the sample complexity of learning grammar will be investigated. At the same time the evolution of human languages can be modeled as a dynamical system. This can be used as an evolutionary criterion to choose between different linguistic theories (models). ***
