英语翻译1.MOTIVATIONActive learning and semi-supervised learning are both importanttechniques when labeled data are scarce and unlabeleddata are abundant.Active learning targets the situationwhere it is costly to get more labeled data so whichins

英语翻译1.MOTIVATIONActive learning and semi-supervised learning are both importanttechniques when labeled data are scarce and unlabeleddata are abundant.Active learning targets the situationwhere it is costly to get more labeled data so whichins
英语翻译
1.MOTIVATION
Active learning and semi-supervised learning are both important
techniques when labeled data are scarce and unlabeled
data are abundant.Active learning targets the situation
where it is costly to get more labeled data so which
instance(s) should you get labels for in order to get the
best learned model.In such a scenario,we let the learning
algorithm pick a set of unlabeled instances to be labeled
by an oracle (i.e.,human),which will then be used
as (or to augment) the labeled data set.In other words,
we let the learning algorithm tell us which instances to label,
rather than selecting them randomly.Active learning is
named as such because the learner actively asks for more labels
in order to increase its efficacy,thereby minimizing the
amount of labeled data needed to get a good model.Semisupervised
learning takes an orthogonal approach to active
learning and instead uses unlabeled data to help supervised
learning tasks.The name “semi-supervised learning” comes
from the fact that the data used is between supervised and
unsupervised learning.Semi-supervised learning promises
higher accuracies with less annotating effort.Various semisupervised
learning methods have been proposed and show
promising results.For an overview of methods,there is a
good regularly-updated survey available on semi-supervised
learning [36].Combining these two learning frameworks
seems intuitively to make sense,yet we are aware of only
one such pairing [38].

英语翻译1.MOTIVATIONActive learning and semi-supervised learning are both importanttechniques when labeled data are scarce and unlabeleddata are abundant.Active learning targets the situationwhere it is costly to get more labeled data so whichins
1.动机
主动学习和半监督学习都很重要
当标签数据的技术稀少,无标签
资料丰富.主动学习目标的情况
它是昂贵的地方获得更多的数据,这样的标记
实例（s）应你的标签,以便获得
最好的学习模式.在这种情况下,我们让学习
算法选择一个未标记的实例设置为标记
由甲骨文公司（即人类）,这将被用来
为（或增加）的标记数据集.换句话说,
让我们学习算法告诉我们,到标签的实例,
随机选择,而不是他们.主动学习
这种命名为学习者积极,因为更多的标签要求
为了增加其疗效,从而最大限度地减少
标记的数据量需要得到一个很好的模式. Semisupervised
学习需要一个为主动正交方法
学习,而是使用无标签数据,以帮助监管
学习任务.命名为“半监督学习”来
从这一事实中使用的数据之间的监督和
无监督学习.半监督学习的承诺
以较少的努力标注精度较高.各种semisupervised
学习方法被提出,并显示
可喜的成果.对于一个方法的概述,有一
良好的定期更新调查可在半监督
学习[36].结合这两种学习框架
似乎直觉是有道理的,但我们只知道
一个这样的配对[38].
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