### TABLE 1: 1000 RMSE of parameter estimates, averaged over 100 sim- ulations, at optimal value of n0. The smallest value in each row, excluding MCMC, is highlighted. (MCMC was too slow to apply for MA(1) and ARMA(1,1) processes when n = 1000.) CPU times are for a SunUltra2 to process 100 series of length 100 for each estimation method. 5 Discussion Latent Gaussian processes are exible models for categorical behaviour data. In particular, we have seen that a latent ARMA(2,1) process shows

### Table 1. Use of Requirement-Based Re-Engineering Products

### Table 1: Speaker identification errors for the Gaussian mixture model (GMM), the probabilistic latent semantic analysis model (PLSA) and the regularized probabilis- tic latent semantic analysis model (RPLSA). Test Data

2005

"... In PAGE 8: ... To compare the algorithms in a wide range we tried various lengths of test data. The results are shown in Table1 . Clearly, both PLSA and RPLSA are more effective than the GMM in all cases.... ..."

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### Table 1: Speaker identification errors for the Gaussian mixture model (GMM), the probabilistic latent semantic analysis model (PLSA) and the regularized probabilistic latent semantic analysis model (RPLSA). Test Data

2005

"... In PAGE 8: ... Specifically, three pieces of test speech from each speaker that have the lengths of 2, 3 or 5 seconds were used in each experiment. The results are shown in Table1 . Clearly, both PLSA and RPLSA are more effective than the GMM in all cases.... ..."

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### Table 1. Results obtained on gaussian and discrete data. The accuracy confidence intervals was computed by repeating the data generation, learning and classification steps 100 times for gaussian variables and 60 times for discrete variables.

"... In PAGE 10: ... 5.2 Results The results obtained with the Gaussian model and for the discrete model are shown in Table1 . Accuracy and Brier score are reported with their 95% confi- dence intervals.... ..."

### Table 8. Fraud Data Set, Categorical Data, Logistic Regression

"... In PAGE 6: ... This yielded a very good correct classification rate, as only 22 of 1000 test cases were fraudulent. Table8 gives results for the logistic regression model. Table 8.... ..."

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### Table 2 (Continued)

"... In PAGE 11: ...Table2 Summary of reuse approaches Approach Definition Findings Development scope Whether the reusable assets are from a source internal or external to the project (Frakes and Terry 1996). Examples of externally developed assets are those developed in other projects, COTS or OSS components.... In PAGE 31: ... (2000), when reusable assets are developed, explicit reuse process and reuse team. See Table2 for definitions. Criteria for selection of metrics Related work, precise definitions and how they should be linked to the propositions of the study.... ..."

### Table 2. Categorical Loan Data, Logistic Regression

"... In PAGE 4: ... Note that this trend was not true throughout the experiment, as when the training set was reduced to 150 cases, the correct classification rate actually increased over the results for training set sizes of 180 and 225. Table2 shows the results of the logistic regression model on categorical data. Table 2.... ..."

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### Table 3: Thesaurus groups related to telecommunication and their translation generated based on a class-based translation model.

"... In PAGE 6: ...Table3 for examples of synonyms generated for words related to the domain of telecommunication regulation. Table 3: Thesaurus groups related to telecommunication and their translation generated based on a class-based translation model.... ..."

### Table 1. Categorical Loan Data, Decision Tree

"... In PAGE 3: ...otal), 0.2 (225 total), 0.25 (180 total), and 0.3 (150 total). The correct classification rates and cost results are shown in Tables 1 through 6. The first test is shown in Table1 , using a decision tree model on categorical data. Table 1.... ..."

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