Sentence examples for the changed model from inspiring English sources

We can score the performance of the classifiers on the changed model by the posterior probability.

Therefore, to solve these issues, we propose an evolutionary classifier with a Bayes kernel (BYEC) that can be adjusted with small sample sets to fit the changed model.

However, to avoid information loss, we should set the BYEC classifier with a smaller k value when the changed model has a small bias with the original model.

Moreover, the above model can be reused to facilitate the rapid mid-surface abstraction of the changed model based on its hierarchical semantic structure.

We compared the performance of this method to various algorithms; experimental results demonstrate that the proposed method can be adjusted with a small sample set to fit the changed model.

With the advantages and disadvantages of the BYEC algorithm, in a new production line, we can use the original BYEC algorithm without any labeled samples on the changed model; with the growth of the sample set size, we can adjust the BYEC model to become more adaptive.

The combination of the subclassifiers is very important to this evolutionary classifier because it not only response for improving the performance of the final integrated classifier but also for the ability to evolve itself to fit the new changed model.

I asked Hamilton whether that revenue model was tenable, given the changed distribution model of the Mac App Store, which seems to make users expect free updates for life, and a move towards freemium and SaaS models from a lot of other software vendors.

In the model technical report [ 21] a large number of analyses on model parameters were reported, and, naturally, changes in model parameters lead to changed model estimates.

Second, we adopt multiple SVMs to predict individual features, enabling our evolutionary Bayes kernel to fit well with a small sample set for the changed production model.

Thus, we built a classic classifier with an evolutionary Bayes kernel and adjusted the kernel with a small sample set for the changed production model rather than training every product and every production line with a single data set.