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pro vyhledávání: '"Russell Greiner"'
Publikováno v:
Artificial Intelligence. 170:19-58
Many tasks require evaluating a specified Boolean expression @f over a set of probabilistic tests whose costs and success probabilities are each known. A strategy specifies when to perform which test, towards determining the overall outcome of @f. We
Publikováno v:
Cheng, J, Greiner, R, Kelly, J, Bell, D & Liu, W 2002, ' Learning Bayesian networks from data: An information-theory based approach ' Artificial Intelligence, vol. 137 (1-2), pp. 43-90 . DOI: 10.1016/S0004-3702(02)00191-1
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requi
Autor:
Russell Greiner
Publikováno v:
Artificial Intelligence. 107:175-217
A knowledge-based system uses its database (also known as its “theory”) to produce answers to the queries it receives. Unfortunately, these answers may be incorrect if the underlying theory is faulty. Standard “theory revision” systems use a
Publikováno v:
Artificial Intelligence. 97:345-380
Most learning algorithms work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking proc
Autor:
Russell Greiner
Publikováno v:
Artificial Intelligence. 84(1-2):177-208
Many learning systems search through a space of possible performance elements, seeking an element whose expected utility, over the distribution of problems, is high. As the task of finding the globally optimal element is often intractable, many pract
Autor:
Charles Elkan, Russell Greiner
Publikováno v:
Artificial Intelligence. 61:41-52
The book under review here, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project, describes progress so far in an attempt to build a system that is intended to exhibit general common-sense reasoning ability. This re
Autor:
Russell Greiner
Publikováno v:
Artificial Intelligence. 50:95-115
A backward chaining process uses a collection of rules to reduce a given goal to a sequence of database retrievals. A “derivation strategy” is an ordering on these steps, specifying when to use each rule and when to perform each retrieval. Given
Autor:
Russell Greiner
Publikováno v:
Artificial Intelligence. 35:81-125
This research describes a method for learning by analogy — i.e., for proposing new conjectures about a target analogue based on facts known about a source analogue. We formalize this process as a rule of plausible inference and present heuristics w
Publikováno v:
Artificial Intelligence. 41:79-88
Reiter [3] has developed a general theory of diagnosis based on first principles. His algorithm computes all diagnoses which explain the differences between the predicted and observed behavior of a given system. Unfortunately, Reiter's description of
Publikováno v:
Artificial Intelligence. (2):137-174
Most classification algorithms are “passive”, in that they assign a class label to each instance based only on the description given, even if that description is incomplete. By contrast, an active classifier can—at some cost—obtain the values