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Table 12: Evaluation of Data-Driven Methods

in Resource Estimation in . . .
by Lionel C. Briand, Isabella Wieczorek 2002
Cited by 5

Table 2: Results of applying data-driven simpli cation.

in Simple and Effective Link-Time Optimization of Modula-3 Programs
by Mary Fernandez, Mary F. Fern'andez 1995
"... In PAGE 6: ... Results. Table2 summarizes the results of apply- ing data-driven simpli cation to our benchmarks. Each program and the runtime system were compiled into mill with m3 and mlcc and linked with mld.... ..."
Cited by 86

TABLE 8: UNIVARIATE GAUSSIAN ESTIMATES WITH DATA-DRIVEN s

in Semiparametric Fractional Cointegration Analysis
by D. Marinucci, P. M. Robinson 2001
Cited by 1

Table 1. Classification of sciences by motivation for international collaboration Data-driven

in Address for correspondence:
by Caroline S. Wagner, Caroline S. Wagner 2004
"... In PAGE 4: ...6 Scientometrics 62 (2005) Table1 provides a notional concept of this typology, showing some fields of science as they would be classified within this scheme. Table 1.... ..."

Table 6: Results using the data-driven system on failed sentences

in Parsing of Grammatical Relations for Databases of Spoken Language
by Kenji Sagae

TABLE 8: UNIVARIATE GAUSSIAN ESTIMATES WITH DATA-DRIVEN s

in SEMIPARAMETRIC FRACTIONAL COINTEGRATION ANALYSIS*
by unknown authors 1999

TABLE 5. Performance contributions of data-driven sequencing and integration.

in
by unknown authors

Table 1 | Comparison of data-driven modelling approaches Data-driven

in Research and Departments of
by Kevin A. Janes, Michael B. Yaffe

Table 1: Relative entropies for model-driven and data-driven FAN classifiers.

in Using Background Knowledge to Construct Bayesian Classifiers for Data-Poor Domains
by Marcel Van Gerven, Peter Lucas 2004
"... In PAGE 7: ... Next to the occurence of such discrepancies, which can only be identified by having sufficient knowledge about the domain, the construction of an accurate classifier based on a small database is impaired in principle. The conjecture that suboptimal dependencies were added is supported by the increasing relative entropy between the declarative model and data-driven classifiers with increasing structural complexity ( Table1 ). It is unlikely that the naive classifier is simply the best representation of the dependencies within the model since relative entropy was shown to decrease for model-driven classifiers of increasing structural complexity.... ..."

Table 1. Data-driven feature clustering for PhoneBook. (e: log energy; c: MFCCs; : deltas)

in FEATURE PRUNING IN LIKELIHOOD EVALUATION OF HMM-BASED SPEECH RECOGNITION
by unknown authors
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