Sentence examples for estimation benchmarks from inspiring English sources

We briefly introduce the gait-based age estimation benchmarks in Section 4, and present various performance evaluations using our dataset in Section 5. Finally, we conclude this paper in Section 6 and discuss future work on the subject.

We demonstrated our dataset's validity through experiments with gait-based age estimation benchmark algorithms and investigated the dependences of age estimation performance on gender and age group, in addition to the number of training subjects.

Although some kind of upper limit on the performance of gait-based age estimation using benchmarks was demonstrated in the previous section, it is still meaningful to investigate the correlation between benchmarks for further performance improvement using a fusion scheme.

The benchmark estimation results are presented in Table 1.

In column (1) we report the benchmark estimation with the dummy "Retired" as the dependent variable.

Hence, the outcome of this study may be useful for benchmarking estimation uncertainty for future events empirically.

This work is concerned with the minimum variance (MV) benchmark estimation without a prior knowledge of time-delay.

εMissing coefficient due to perfect predictability of underweight; hence optimal weight and underweight are combined in this estimation as a benchmark.

In order to capture part of these unobserved individual characteristics, we control for a wide range of observed time-varying and time-invariant individual characteristics in the benchmark estimation.

DEA is a mathematical programming technique which enables us to evaluate a specific process which is based on the estimation of a benchmark frontier a relative frontier against which the decision making units (DMUs) are assessed, using specified DMUs' inputs and outputs (Daraio and Simar 2007).

For parameter estimation only, several benchmark problems are publicly available, e.g. in the data base EASY-FIT (Schittkowski, 2002).