Sentence examples for based on supervised and from inspiring English sources

Context recognition algorithms based on supervised and unsupervised learning methods primarily use probabilistic and statistical reasoning.

We thus propose approaches for automated decision-making based on supervised and reinforcement learning.

For a detailed description and discussion of these steps, including an account of data mining techniques, algorithms, and tasks based on supervised and unsupervised methods, the interested reader can be directed to [55].

Moreover, the algorithm presented by Tastan et al. is based on supervised learning and training from known interactions between HIV-1 and human proteins.

Our approach is based on supervised learning and dimensionality reduction techniques that allow the representation of high dimensional nonlinear actions to linear latent low dimensional spaces.

To exploit the information redundancy that exists among the huge number of variables and improve classification accuracy of microarray data, we propose a gene selection method, Recursive Feature Addition (RFA), which is based on supervised learning and similarity measures.

Based on supervised learning and similarity measurements, we propose a Recursive Feature Addition (RFA), recursively employ supervised learning to obtain the highest training accuracy and add a subsequent gene based on the similarity between the chosen features and the candidates to minimize the redundancy within the feature set.

g c ∈ C } In the methods mentioned above, a feature is recursively added to the chosen feature set based on supervised learning and the similarity measures.

We refer to the proposed method as supervised clustering because after generation of the clusters we narrow down the candidate clusters for further analysis based on supervised learning and thus improve the accuracy of prediction of the proposed method.

The source separation algorithm is based on supervised machine learning and time-frequency masking, and the design of the system has been carried out considering the power and computational limitations of state-of-the-art hearing aids.

This paper presents a novel sound source separation algorithm for binaural speech enhancement based on supervised machine learning and time-frequency masking.