Sentence examples for learning estimation from inspiring English sources

The active learning method (optimal design method) is to design inputs to improve a learning (estimation) accuracy for the case where we can design the inputs purposefully.

In the "Dynamic treatment regimes and Q-learning" section, we formalize DTRs and introduce the Q-learning estimation algorithm.

As a compromise, we learn for estimation of α.

After the structure of the network was learned, the estimation of the parameters of the local distributions was performed using maximum likelihood.

Following the work of Ravikumar et al. (2010), Jalali et al. (2011) and Allen and Liu (2013), we approach the problem of Markov network structure learning via neighborhood estimation, where we obtain the global network estimate E ^ by stitching together the estimated neighborhoods of the nodes.

Novel incremental ensemble learners are explained along with one of the largest ensemble learning (in effort estimation) experiments yet attempted.

In order to implement the proposed method, we use alternating direction method of multipliers (ADMM) for simultaneous Bayesian nonparametric dictionary learning and image estimation.

The improved particle filter was proposed by Yoo et al. [10] by incorporating semi-supervised machine learning for location estimation in mobile sensor networks.

In fact, in traditional online learning an accurate estimation of c ¯ requires a sample-path of observations where each state in S is visited a considerable number of times.

So, and by reviewing the model description initiated in Section 2.3, it is important to highlight that at least four elements concerning to a cognitive process have been utilized: perception (primary network view), awareness (feedback awaiting), learning (coherence time estimation), and judgment (opportunistic transmission nulls/peaks).

Second, models are estimated with neural network learning algorithms and the estimation of the models benefits from algorithm cooperation weight decay and early stopping.