Sentence examples for instance in the data from inspiring English sources
Similar techniques for abstracting NLG input data from the expected output strings were applied, for instance, in the data preparation for the First Surface Realisation Challenge Task [12].
In data classification, the algorithms receive as input a given training data set, denoted here X = { inp1,…ab1), inpnp n,lab n )}, where the pair (inp i,lab i ) is the i-th data instance in the data set.
In the data set selected for the experiments, no instance belonging to novel class was declared as existing class instance in the data set and very few instances belonging to existing class were declared as novel class instance.
Furthermore, we computed the each model's Brier score as a performance measure [ 24], which is the mean squared difference between the observed outcome and the predicted probability for each instance in the data set.
However, in practice, such a hyperplane may not be found because of the specific distribution of instances in the data.
Secondly, we saved a well trained classifier instead of the whole instances in the data chunk which cost much less memory.
In many data stream mining applications where data exhibit a time series nature, the goal is to predict information about future instances in the data stream given some knowledge about previous ones.
(E(P_i)) and (E(P_h)) represent the entropy of the produced partition and the ground truth partition, and N is the total number of instances in the data set.
In order to measure the statistical significance of the improvements, we apply the concept of Confidence Interval defined by Eq. 1 [29], P is the accuracy rate, and N is the amount of instances in the data set: more than 130,000 in the REALDISP dataset.
Each line in a cost curve consists of many classifiers that come from two variables: different thresholds and different numbers of positive instances in the data set.
In a simpler interpretation, Cost Curve plots represent the cost probability, equivalent [19] to the percentage of positive instances in the data set to which the classifier is applied, versus the normalized expected cost, equivalent to the ratio of the mistakes both in terms of false positives as well as false negatives.
