twenty years have brought many changes but the song remains the same. The title of the book indicates that as we develop the theory, we will focus our attention on examples. Hoping that the book would be a useful reference for people who apply probability in their work, we have tried to emphasize

, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper

In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been

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, incentive contracts, and other topics. Appendices contain technical details, some simple worked examples, and a bibliography for each section.

with worked examples involving the learning of probability distributions over sequences as well as the learning of simple forms of uncertain knowledge.

of research, and inspired several successful change projects involving the redesign of work (e.g., For one, a number of researchers have been unable to provide empirical support for the major tenets of the two-factor theory itself (see, for example, Moreover, the theory does not provide for differences

Worked examples are instructional devices that provide an expert's problem solution for a learner to study. Worked-examples research is a cognitive-experimental program that has relevance to classroom in-struction and the broader educational research community. A frame-work for organizing

Abstract Worked-examples play an important role in learning and problem solving and are crucial to cognitive skill acquisition. However students' learning styles are a factor in how much they are willing to expend serious effort on understanding worked-examples, with active students tending

, especially for small training sets, cutting the number of labeled training examples down to a twentieth on some tasks. This work also proposes an algorithm for training TSVMs efficiently, handling 10,000 examples and more.