Showing papers by "Center for Discrete Mathematics and Theoretical Computer Science published in 1999"

Recognition of a protein fold in the context of the SCOP classification

Abstract: A computational method has been developed for the assignment of a protein sequence to a folding class in the Structural Classification of Proteins (SCOP). This method uses global descriptors of a primary protein sequence in terms of the physical, chemical, and structural properties of the constituent amino acids. Neural networks are utilized to combine these descriptors in a way to discriminate members of a given fold from members of all other folds. An extensive testing of the method has been performed to evaluate its prediction accuracy. The method is applicable for the fold assignment of any protein sequence with or without significant sequence homology to known proteins. A WWW page for predicting protein folds is available at URL http://cbcg.lbl.gov/. Proteins 1999;35:401–407.

Concept Learning and Feature Selection Based on Square-Error Clustering

Abstract: Based on a reinterpretation of the square-error criterion for classical clustering, a “separate-and-conquer” version of K-Means clustering is presented and a contribution weight is determined for each variable of every cluster The weight is used to produce conjunctive concepts that describe clusters and to reduce or transform the variable (feature) space

Optimal, Distributed Decision-Making: The Case of No Communication

Abstract: We present a combinatorial framework for the study of a natural class of distributed optimization problems that involve decision-making by a collection of n distributed agents in the presence of incomplete information; such problems were originally considered in a load balancing setting by Papadimitriou and Yannakakis (Proceedings of the 10th Annual ACM Symposium on Principles of Distributed Computing, pp. 61-64, August 1991). For any given decision protocol and assuming no communication among the agents, our framework allows to obtain a combinatorial inclusion-exclusion expression for the probability that no "overflow" occurs, called the winning probability, in terms of the volume of some simple combinatorial polytope. Within our general framework, we offer a complete resolution to the special cases of oblivious algorithms, for which agents do not "look at" their inputs, and non-oblivious algorithms, for which they do, of the general optimization problem. In either case, we derive optimality conditions in the form of combinatorial polynomial equations. For oblivious algorithms, we explicitly solve these equations to show that the optimal algorithm is simple and uniform, in the sense that agents need not "know" n. Most interestingly, we show that optimal non-oblivious algorithms must be non-uniform: we demonstrate that the optimality conditions admit different solutions for particular, different "small" values of n; however, these solutions improve in terms of the winning probability over the optimal, oblivious algorithm. Our results demonstrate an interesting trade-off between the amount of knowledge used by agents and uniformity for optimal, distributed decision-making with no communication.