IBA-II: Another Item-Based Algorithm of Learning from Examples

Abstract

Another novel algorithm of learning from examples is presented. A significant deference between a traditional algorithm with the new item-based algorithm is that the traditional algorithm must scan in example space (i.e. scanning the given examples one by one) to obtain the needed heuristic information, while the new item-based algorithm scans in item space (i.e. scanning items one by one) and then executes some simple calculations to obtain the same heuristic information as the traditional algorithm to do. Owing to the two facts that an item can contain thousands of examples and that the time expanded on scanning an item equals to the time on an example, the ability of the new algorithm has been revolutionarily increased, so that it can treat efficiently with the learning tasks with mass data, with which the traditional algorithms cannot deal.