Meta-Learning Approaches for Recovery Rate Prediction

Machine learning approaches for epitope prediction

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‘Meta’ Approaches to Protein Structure Prediction

The computational assignment of three-dimensional structures to newly determined protein sequences is becoming an increasingly important element in experimental structure determination and in structural genomics (Fischer et al. 2001a). In particular, fold-recognition methods aim to predict approximate three-dimensional (3D) models for proteins bearing no evident sequence similarity to any prote...

Feed-forward Nonlinear Network Approaches for Mpeg Video Rate Prediction

Conventional approaches generally assume that the compressed video has high correlation so that linear predictive methods can be applied. However , for realistic videos such as movies, sports and advertisements, there can be many exceptions since the correlation may be abnormally low. In this paper, we developed a feed-forward network-based rate control scheme which eeec-tively accommodates dra...

Learning the Learning Rate for Prediction with Expert Advice

Most standard algorithms for prediction with expert advice depend on a parameter called the learning rate. This learning rate needs to be large enough to fit the data well, but small enough to prevent overfitting. For the exponential weights algorithm, a sequence of prior work has established theoretical guarantees for higher and higher data-dependent tunings of the learning rate, which allow f...

A meta‐recovery framework: positioning the ‘New Recovery’ movement and other recovery approaches