Sentence examples for the weight of training from inspiring English sources

In this paper, to overcome the weight problem of FC-Ranker, we deal with the weight of training error as a variable, and employ the primal SVM technique [ 21] to re-formulate the classification problem as the CRanker classification model.

Different with the traditional SVM model, in which the weight of training error is equally contributed by each data sample, FC-Ranker uses a fuzzy classification model to estimate the possibility of each target PSM being correct.

Equation (6) may now be rewritten as (7) s (x )   =   ∑ i = 1 N α i y i   〈 x i,   x 〉   +   b, where α i denotes the weight of training sample x i and y i denotes its corresponding label (±1).

The square error of classifier f (x → i ) is given by: { f (x → i )   − y i } 2 The procedure of Boosting with Bagging is described as following Initialization: α 0 =   1 ; t   =   1 ;   W i   =   p i   =   1 / N where i = 1, 2, 3,…, N; N is the number of training instances; Wi is the weight of training instance; Pi is the probability of instance.

The in-train force is closely relevant to the weight of train configuration and the braking deceleration.

The above-mentioned relationship emphasizes the importance of reducing both the weight of train and its aerodynamic resistance in order to achieve savings in the energy consumption during the longest phase of trip cruising at high speed.

We also consider transferring the weight of trained expert streams for VGG-16 using the good practice approach from [23] and used [12] for the temporal segment network to be gated with our trained VGG-16 gating network.

The weight of trains in North America had greatly increased by the mid-1890s.

By the end of the 19th century, the weight of trains had increased greatly and far exceeded the maximum capacity of the bridge.

Furthermore, the weights of training samples will be adaptively adjusted by the classified results.

Furthermore, the weights of training samples will be adaptively adjusted by the classified results, i.e., component classifiers with lower training errors will gain greater weights, and component classifiers with higher training errors will get smaller weights.