We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for i...
We present a novel method for predicting the secondary structure of a protein from its amino acid sequence. Most existing methods predict each position in turn based on a local wi...
Scott C. Schmidler, Jun S. Liu, Douglas L. Brutlag
Many low-level vision algorithms assume a prior probability over images, and there has been great interest in trying to learn this prior from examples. Since images are very non G...
Background: Genes interact with each other as basic building blocks of life, forming a complicated network. The relationship between groups of genes with different functions can b...
Jong-Min Kim, Yoon-Sung Jung, Engin A. Sungur, Kap...
In the previous papers (Pupeikis, 2000; Genov et al., 2006; Atanasov and Pupeikis, 2009), a direct approach for estimating the parameters of a discrete-time linear time-invariant (...