As aims to address larger, more complex tasks, the problem of focusing on the most relevant information in a potentially overwhelming quantity of dat… - Pat Langley

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As aims to address larger, more complex tasks, the problem of focusing on the most relevant information in a potentially overwhelming quantity of data has become increasingly important.

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About Pat Langley

Pat Langley (born May 2, 1953) is an American cognitive scientist and AI researcher, Honorary Professor of Computer Science at the University of Auckland, and Director of the Institute for the Study of Learning and Expertise. He coined the term decision stump and was founding editor of journals Machine Learning and Advances in Cognitive Systems.

Also Known As

Alternative Names: Patrick W. Langley Pat (Patrick) Wyatt Langley
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Science is a seamless web: each idea spins out to a new research task, and each research finding suggests a repair or an elaboration of the network of theory. Most of the links connecting the nodes are short, each attaching to its predecessors. Weaving our way through the web, we stop from time to time to rest and survey the view — and to write a paper or a book.

Given a sample of data S, a learning algorithm L, and a feature set A, feature xi , is incrementally useful to L with respect to A if the accuracy of the hypothesis that L produces using the feature set {xi} ∪ A is better than the accuracy achieved using just the feature set A.

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BACON.4 does not have heuristics for considering trigonometric functions of variables directly . Thus, in the run described here we simply told the system to examine the sines. In the following chapter we will see how BACON can actually arrive at the sine term on its own in a rather subtle manner.

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