Erittäin mielenkiintoista asiaa koneoppimisesta
Computer scientist Tom Mitchell, director of the Center for Automated Learning and Discovery at Carnegie Mellon University, says machine learning is useful for the kinds of tasks that humans do easily -- speech and image recognition, for example -- but that they have trouble explaining explicitly in software rules. In machine-learning applications, software is "trained" on test cases devised and labeled by humans, scored so it knows what it got right and wrong, and then sent out to solve real-world cases.
Mitchell is testing the concept of having two classes of learning algorithms in essence train each other, so that together they can do better than either would alone. For example, one search algorithm classifies a Web page by considering the words on it. A second one looks at the words on the hyperlinks that point to the page. The two share clues about a page and express their confidence in their assessments.
Mitchell's experiments have shown that such "co-training" can reduce errors by more than a factor of two. The breakthrough, he says, is software that learns from training cases labeled not by humans, but by other software. [...]
[...] Stuart Russell, a computer science professor at the University of California, Berkeley, is experimenting with languages in which programmers write code for the functions they understand well but leave gaps for murky areas. Into the gaps go machine-learning tools, such as artificial neural networks. Russell has implemented his "partial programming" concepts in a language called Alisp, an extension of Lisp.