In this note we consider a simple reformulation of the traditional power iteration algorithm for computing the stationary distribution of a Markov chain. Rather than communicate t...
We present an approach to modeling the average case behavior of learning algorithms. Our motivation is to predict the expected accuracy of learning algorithms as a function of the...
Traditional design representations are inadequate for generalized reasoning about modularity in design and its technical and economic implications. We have developed an architectu...
This paper presents an investigation into the classification of a difficult data set containing large intra-class variability but low inter-class variability. Standard classifiers...
It is well-known that, in unidentifiable models, the Bayes estimation provides much better generalization performance than the maximum likelihood (ML) estimation. However, its ac...