With all samples represented by a feature vector, now it is possible for us to construct our predictor using the machine learning approach.
Fig. 10 Design of autonomic elements (Cloud workload pattern and predictor) using strategy design patter.
We developed a thioredoxin predictor using the approach of integrating sequence with structural information.
We train our predictor using the statistical model of PU because we are doing analysis on the offline data.
Dragoi and Dinu's method [9] and Lee et al.'s method [20] utilize the LS predictor using the different shape of training set and support pixels.
The standard least-squares error of a linear predictor, using weight vector and ignoring a constant term for the output variance, is calculated by (B1).
The SPN predictor using CAI4 [13] is based on the CAI [14] interpolation algorithm which is adapted from the gradient-adaptive predictor (GAP) [15].
The initial Gaussian anomaly predictor using only content information (i.e. the data stream itself) was built using all the sensor values in the training dataset.
False-positive rates (FPRs, corresponding to 1-specificity) of poor neurological outcome were calculated for each significant predictor, using an exact binomial 95% confidence interval (CI).
Based on the developed model, an optimal multivariable controller with a tracker based linear quadratic state feedback algorithm and a predictor using steepest descent method is designed.
Our developed support vector machine-based predictor using dipeptide composition is shown to qualitatively predict PDZ domain-peptide interaction with a high accuracy rate.
