In 2003, Robinson's article in
Linux Journal detailed a new approach to
computer programming perhaps best described as a
general purpose classifier which expanded on the usefulness of
Bayesian filtering. Robinson's method used math-intensive
algorithms combined with
Chi-square statistical testing to enable computers to examine an unknown file and make intelligent guesses about what was in it. The method became the basis for
anti-spam techniques used by Tim Peters and Rob Hooft of the influential
SpamBayes project. Spamming is the abuse of electronic messaging systems to send unsolicited, undesired bulk messages. SpamBayes assigned probability scores to both
spam and
ham (useful emails) to guess intelligently whether an incoming email was spam; the scoring system enabled the program to return a value of
unsure if both the
spam and
ham scores were high. Robinson's method was used in other anti-spam projects such as
SpamAssassin. Robinson commented in
Linux Journal on how fighting spam was a collaborative effort: In 1996, Robinson patented a method to help marketers focus their online advertisements to consumers. He explained: ==Entrepreneurial activity==