Statistical Generalization of Performance-Related Heuristics for Knowledge-Lean Applications

Ieumwananonthachai, Arthur; Wah, Benjamin W.

doi:10.1007/978-3-662-03423-1_17

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…log( 0 0 ;j )] 10when HM 0 0 is used as the baseline instead. Since the terms in the summations in (10) are constants that depend only on the baseline HMs, the ordering of the set of HMs remain unchanged when the baseline HM is switched.…”

Section: Normalization Methods Without Anomaliesmentioning

confidence: 99%

Genetics-Based Learning and Statistical Generalization

Wah¹,

Ieumwananonthachai²,

Yu³

1997

Knowledge-Based Systems

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Section: Normalization Methods Without Anomaliesmentioning

confidence: 99%

Genetics-Based Learning and Statistical Generalization

Wah¹,

Ieumwananonthachai²,

Yu³

1997

Knowledge-Based Systems

Self Cite

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“…To address uncertainties in using sample means, we have studied a concept called probability of win [39], P win , that compares two sample means and computes the probability that one sample mean is larger than another. This is similar to hypothesis testing in which we take random samples to test whether a property of a population is likely to be true or false [15]. Obviously, it may be difficult to test a hypothesis fully by testing the entire population of test cases or by testing only a single random sample.…”

Section: Generalizability Measures Across Subdomainsmentioning

confidence: 99%

“…Probabilities of mean have been used to evaluate generalizability in various genetics-based learning and generalization experiments [39], [15], [34], [24], [41], [40], [16], [38]. These include the learning of load balancing strategies in distributed systems and multicomputers, the tuning of parameters in VLSI cell placement and routing, the tuning of fitness functions in genetics-based VLSI circuit testing, the automated design of feedforward neural networks, the design of heuristics in branch-and-bound search, range estimation in stereo vision, and the learning of parameters for blind equalization in signal processing.…”

Section: ) No Hypothesis Is Better Than H 0 In All Subdomainsmentioning

confidence: 99%

Generalization and generalizability measures

Wah

1999

IEEE Trans. Knowl. Data Eng.

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In this paper, we define the generalization problem, summarize various approaches in generalization, identify the credit assignment problem, and present the problem and some solutions in measuring generalizability. We discuss anomalies in the ordering of hypotheses in a subdomain when performance is normalized and averaged, and show conditions under which anomalies can be eliminated. To generalize performance across subdomains, we present a measure called probability of win that measures the probability whether one hypothesis is better than another. Finally, we discuss some limitations in using probabilities of win and illustrate their application in finding new parameter values for TimberWolf, a package for VLSI cell placement and routing. Index Terms-Anomalies in generalization, credit assignment problem generalization, machine learning, subdomains, probability of win, VLSI cell placement and routing.

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