Sentence examples for multiple learning algorithms from inspiring English sources

While significant research has been conducted in model-based and data-driven prognostics, there has been little research reported on the RUL prediction using an ensemble learning method that combines prediction results from multiple learning algorithms.

Models have been built using multiple learning algorithms including support vector machine and random forest.

They evaluated the prediction performance of each feature category using multiple learning algorithms and reported an accuracy of 55% for gender prediction and 24% for age prediction using only isolated link features.

Ensemble methodology is an efficient technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuracy [ 15].

One kind constructs a set of base learners called homogeneous base learners with a single base learning algorithm; the other kind produces base learners by adopting multiple learning algorithms, which are called heterogeneous learners.

Multiple kernel learning algorithms combine kernels that naturally correspond to different views, either linearly [53] or nonlinearly [54, 55] to improve learning performance.

Similarly, Herrera-Yagüe and Zufiria applied multiple machine learning algorithms on 22 features incorporating node-level activity, ego-network strucuture and homophily to predict age and gender [45].

Multiple machine learning algorithms likewise showed that the novel biomarker panels improved the diagnostic performance of the current leading biomarkers.

Using alternative statistical strategies that are more amenable to the analysis of larger combinations of markers, multiple machine learning algorithms likewise showed that the novel biomarkers improved upon the diagnostic performance of the traditional biomarkers (Aβ42, tau, p-tau181).

Finally, applying multiple machine learning algorithms to our task might be robust for evaluation of prediction performance.

Furthermore, MNet constructs a composite network that is coherent to all the labels, whereas most multiple kernel learning algorithms optimize a composite kernel for each binary label, or optimize the composite kernel and the classifier in two separative objectives.