Several bite-size results that deal with learning algorithms and approaches.
"Explanation-based learning" (EBL) systems attempt to improve the performance of a PROBLEM SOLVER (PS), by first examining how PS solved previous problems, then modifying PS, to enable it to solve similar problems better (typically, more efficiently), in the future. This article first motivates and explains this explanation-based learning task, then discusses the challenges that any EBL system must address, and presents several successful implementations. It concludes by distinguishing this learning task from other types of learning.
(See also material in EffectivePerformanceSystems.)
Many significant real-world classification tasks involve a large number of categories which are arranged in a hierarchical structure; for example, classifying documents into subject categories under the library of congress scheme, or classifying world-wide-web documents into topic hierarchies. We investigate the potential benefits of using a hierarchy over base classes to learn accurate multi-category classifiers for these domains.
First, we consider the possibility of exploiting a class
hierarchy as prior knowledge that can help one
learn a more accurate classifier. We explore the benefits
of learning category-discriminants in a ``hard'' top-down
fashion and compare this to a ``soft'' approach which
shares training data among sibling categories. Our
results show that hierarchies can often help improve
prediction accuracy. One reason for this is simply that a
hierarchy can help constrain the expressiveness of a
hypothesis class in an appropriate manner. However,
more interestingly, we also find that using a hierarchy
can sometimes help improve prediction accuracy even when
controlling for the expressiveness of the hypothesis class.