Decision tree has been widely applied as an important classification tool. This paper surveys several parallel training decision tree strategies and compares their performance.
决策树途径已被广泛用作一种重要的分类工具,本文研究了几种决策树的并行训练策略并对它们的性能进行了比较。
参考来源 - 决策树的并行训练策略 in C·2,447,543篇论文数据,部分数据来源于NoteExpress
Based on the idea of data parallelism, a parallel training model for RBF (radial basis function) neural network in time-series prediction to improve the training speed is proposed.
根据数据并行的思想,提出了在时序预测中并行训练神经网络的模型,以提高训练速度。
To solve the problems above, this paper brings forward the solutions as follows: Ameliorating the capability of computer-based translation by better employment of existing parallel training corpus.
针对以上问题,本文提出以下方法:通过更好地利用现有平行训练语料去改善统计计算机翻译的性能。
This paper applies parallel tangents of nonlinear programming during the weights training of neural network and puts forward a neural network in view of fast learning algorithm.
因此,作者将非线性规划的平行切线算法用于神经网络的权值学习,提出了一种具有快速学习算法的神经网络。
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