Sentence examples for implementing the machine from inspiring English sources

Some important points regarding the comparison of univariate time series forecasting methods and additional concerns introduced when implementing the machine learning ones (hyperparameter optimization and lagged variable selection) in one- and multi-step ahead forecasting are illustrated in the latter study.

It is a significant contribution because it addressed the limitation in the study by [4] which did not implement the machine learning phase as a distributed process.

Ten python scripts implementing the different machine learning algorithms.

However, the mechanism provides a heuristic approach for allocating the TXOP duration based on the feedback queue size by implementing the finite state machine to dynamically adjust the TXOP duration for each SI.

Using the P RMSD, P ΔS, and P Geom calculated from known disulfide bonds as variables, the prediction model was trained and optimized by implementing the Support Vector Machine method (SVM) (Lin and Chang 2011).

In this study, we proposed a novel Hadoop-based approach to predict drug combinations by implementing the support vector machine and naïve Bayesian classifiers using the MapReduce programming model, which can advance the improvement of scalability of the prediction algorithm.

Hence it permitted to focus the redesign process on the critical groups in order to implement the desired machine upgrade by means of limited modifications to the current machine version.

When someone says general AI or strong AI, you should ask - are you referring to the technologies that implement the intelligent machine or the ultimate goal itself?

At the end of the MLSS, each student should be able to utilize TensorFlow to implement the latest machine learning methods for analysis of images, video and natural language (text).

It consists of a UHF RFID reader IC acting as the RF front end and an FPGA with a soft-core CPU that implements the state machine for receiving the tag signals.

To implement the Support Vector Machine classifier we used the SVMLight (http://www.cs.Cornell.edu/people/tj/svm_light/) package with a radial basic function (RBF) kernel: K(x i, x j) = exp(– G║ x i – x j║)) In this manner, two parameters are crucial to the performance of the classifier: the soft-margin penalty (C) and the radius.