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Table 2: Verwendete Regeln und Anwendungshau gkeiten auf einem Worterbuch von 9149 Worten

in Regelbasiert generierte Aussprachevarianten für Spontansprache
by Thomas Kemp

Table 4: Fine classi cation summary comparison var- ious models compared to the expert rules. Method Type Match Des. Undes.

in A Case Study in Knowledge Discovery and Elicitation in an Intelligent Tutoring Application
by Ann Nicholson, Tal Boneh, Tim Wilkin, Kaye Stacey, Liz Sonenberg, Vicki Steinle
"... In PAGE 7: ...6%). The per- centages of match, desirable and undesirable change are shown in Table4 (set 2, row 1). They are compa- rable with the expert BN 0-N and only slightly worse than the expert BN H/M/L results.... In PAGE 7: ... In this case LZEs were all grouped with ATEs, as were AMOs. The match results are shown in Table4 (set 2, rows 2 and 3). Clearly, summarising the results of 24 DCT into types gives relatively poor performance; it is proposed that this is because many pairs of the classes are dis- tinguished by student behaviour on just one item type, and SNOB might consider these di erences to be noise within one class.... In PAGE 7: ... The data was randomly divided into ve 80%-20% splits for training and testing; the training data was used to parame- terise the expert BN structures using the Netica BN software apos;s parameter learning feature5, while the test data was given to the resultant BN for classi cation. The match results (averaged over the 5 splits) for the ne classi cation comparison of the expert BN struc- tures (with the di erent type values, 0-N and H/M/L) with learned parameters are shown in Table4 (set 3), with corresponding prediction results (also averaged over the 5 splits) given shown in Table 5 (set 2). The average prediction probabilities for the BN with learned parameters are better than for the expert BNs for the O-N type values (0.... In PAGE 8: ....4 to 2.2, while the number of parameters varies from about 700 to 144,000; the structures produced for the H/M/L data seem simpler using these measures, but this is not statistically signi cant. The percentage match results comparing the CaMML BN classi cations (constrained and unconstrained, O- N and H/M/L) are also shown in Table4 (sets 4 and 5), with the prediction results shown in Table 5 (sets 3 and 4). The prediction results for both 0-N and H/M/L are similar to those of the fully elicited expert BNs.... ..."

Tableaux hat, stellt sich die Frage nach einer kombinatorischen Interpretation der Schubertpolynome, die im Spezialfall Schurpolynom wieder die Menge der Tableaux ist. Eine Antwort auf diese Frage gibt der nachste Abschnitt. 2.2 Worte, Tableaux und Schubertpolynome Es werden zuerst grundlegende Begri e vorgestellt, wie sie in verschiedenen Artikeln [LS81.1, LS88.1] von Lascoux und Sch utzenberger de niert wurden. Anschlie end wird die nicht kommutative Theorie der Schubertpolynome betra- chtet.

in unknown title
by unknown authors

Table 1: MIT Delays

in ESTIMATING EFFICACY OF PROGRESSIVE PLANNING FOR AIR TRAFFIC FLOW MANAGEMENT
by R. G. Ingalls, M. D. Rossetti, J. S. Smith, B. A. Peters, Lynne Fellman, James S. Dearmon, Kelly A. Connolly

Table 1: The von Neumann-Halperin Algorithm

in Scalable algorithms for aggregating disparate forecasts of probability
by J. B. Predd, S. R. Kulkarni, H. V. Poor 2006
Cited by 2

Table 1: Beobachtete Typen von Aussprachevarianten

in Regelbasiert generierte Aussprachevarianten für Spontansprache
by Thomas Kemp

Table 4: FEA Von Mises Results

in unknown title
by unknown authors

Table 3: DES instances

in Integrating Equivalency Reasoning into Davis-Putnam Procedure
by Chu Min Li 2000
Cited by 67

Table 4. MIT training data.

in A Bayesian Network Classification Methodology for Gene Expression Data
by Paul Helman, Robert Veroff, Susan R. Atlas, Cheryl Willman 2004
"... In PAGE 23: ...052644 2 2 0.052632 Table4 . MIT training data (continued).... ..."
Cited by 1

Table 4. MIT training data.

in A bayesian network classification methodology for gene expression data
by Paul Helman, Robert Veroff, Susan R. Atlas, Cheryl Willman 2004
"... In PAGE 23: ...052644 2 2 0.052632 Table4 . MIT training data (continued).... ..."
Cited by 1
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