所以我想说,机器学习是引导和启发你从计算学习理论的理论结果。
So I'd say machine learning is guided and inspired by the theoretical results you get from computational learning theory.
同时,将理论结果和实验结果进行比较,得到了样品中分离铜的有效比率。
Meanwhile, theoretical results are compared with experimental results so that the author can get the effective ratios of separate copper in samples.
最后综合应用前面这些理论结果,详细论述了自主研发的GIS系统,包括矢量绘图系统和数据库系统。
Finally, with these theory results, a GIS software develop process is formulated exhausively. This system contains vector drawing sub-system and database sub-system.
It has to be part of a theory, and then trying to adjust it so it can be incorporated, you discover anti-particles.
要想使理论合理,然后试着去调整,使之和实际相符,结果就找到了反粒子
Piaget had a rich theoretical framework, pulling together all sorts of observations in different ways, wrote many, many books and articles and articulated his theory very richly.
通过将各种观察结果,以不同的方式组合在一起,皮亚杰的理论内容变得十分丰富,他写了大量的书籍和论文,丰富了他的理论
They might say my sample period was off, ... but that's what the theory-- ... using my data for the sample period that I computed-- the expected returns and co-variances says one should do.
他们可能说我的采样周期是有问题的,不过我的结果都是靠理论-,我采用自己收集的数据计算出-,预期收益和协方差可以用来指导我们的投资行为。
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