1. An information retrieval method comprising the steps of: generating a term-document matrix to represent electronic information files stored in a computer system, each element in said term-document matrix indicating a number of occurrences of a term within a respective one of said electronic information files; generating, responsive to said term-document matrix, a term-spread matrix, wherein said term spread matrix is a weighted autocorrelation of said term-document matrix, said term-spread matrix indicating an amount of variation in term usage in the information files and, also, the extent to which terms are correlated; receiving a user query from a user, said user query consisting of at least one term; in response to said user query, generating a user query vector, wherein said user query vector has as many elements as the rows of the term-spread matrix; generating, responsive to said user query vector, an error-covariance matrix, wherein said error-covariance matrix reflects an expected degree of uncertainty in the initial choice of keywords of said user; formulating, responsive to said term-spread matrix, error-covariance matrix, and user query vector, a constrained optimization problem, wherein the choice of a lambda value equal to a Lagrange multiplier value in said constrained optimization problem determines the extent of a trade-off between a degree of fit and the stability of all solutions to said constrained optimization problem; generating, responsive to said constrained optimization problem, a solution vector including a plurality of document weights, each one of said plurality of document weights corresponding to one of each said information files, wherein each of said document weights reflects a degree of correlation between said user query and the corresponding one of said information files; and providing an information response to said user reflecting said document weights.