The Rocchio relevance feedback algorithm is one of the most popular and widely applied learning methods from information retrieval. Here, a probabilistic analysis of this algorithm is presented in a text categorization framework. The analysis gives theoretical insight into the heuristics used

probabilistic morphological grammar, and use MDL as our primary tool to determine whether the modifications proposed by the heuristics will be adopted or not. The resulting grammar matches well the analysis that would be developed by a human morphologist. In the final section, we discuss the relationship

use of this heuristic coding scheme, we have used our computer vision system to probabilistically characterize facial motion and muscle activation in an experimental population, thus deriving a new, more accurate representation of human facial expressions that we call FACS+. Finally, we show how

. The method uses a novel alignment objective score called a sum-of-pairs breakpoint score, which facilitates accurate detection of rearrangement breakpoints when genomes have unequal gene content. We also apply a probabilistic alignment filtering method to remove erroneous alignments of unrelated sequences

An algorithm for the 3-Satisfiability problem is presented and a probabilistic analysis is performed. The analysis is based on an instance distribution which is parameterized to simulate a variety of sample characteristics. The algorithm assigns values to variables appearing in a given instance

Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and its expectation

Given a boolean formula ! in conjunctive normal form, the satisfiability problem (sat) is to determine whether there is a truth assignment that satisfies !. Since sat is NP-complete, one is interested in efficient heuristics that perform well "on average," or with high probability. The choice

proposes a probabilistic approach. The theoretic analysis and the experimental study show that the proposed approach is simple to implement and guaranteed to find the optimal if resources permit. It is also fast in obtaining results and effective in selecting features that improve the performance

This survey presents a unified and essentially self-contained approach to the asymptotic analysis of a large class of sums that arise in combinatorial mathematics, discrete probabilistic models, and the average-case analysis of algorithms. It relies on the Mellin transform, a close relative

Two algorithms for the k-Satisfiability problem are presented and a probabilistic analysis is performed. The analysis is based on an instance distribution which is parameterized to simulate a variety of sample characteristics. The algorithms assign values to literals appearing in a given instance