Statistical Learning Theory and Algorithms
Final rept. 15 Feb 92-14 Feb 93,
YALE UNIV NEW HAVEN CT DEPT OF COMPUTER SCIENCE
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This project addressed three fundamental areas in statistical learning theory and algorithms 1 a practical and theoretically sound method or estimating generalization performance of nonlinear learning systems Generalized Prediction Error, GPE, 2 a more powerful and efficient class of network architectures Parameterized Projection Pursuit Regression P3R networks, and 3 faster real-time learning methods based on asymptotically optimal stochastic gradient search. Three papers were published under this grant. Additionally, a graduate student finished his PhD under research topic Networks with Learned Unit Response Functions .
- Statistics and Probability