supervised learning造句
例句与造句
- Supervised learning of heuristic function for refutation
反演启发函数的监督学习算法 - The former belongs to supervised learning and the latter belongs to unsupervised learning
它们分属于有监督学习与无监督学习。 - A semi - supervised learning system was proposed based on art ( adaptive resonance theory )
摘要根据自适应谐振理论提出了半监督学习自适应谐振理论系统。 - Supervised learning with the use of regression and classification networks with sparse data sets will be explored
也将在课程中以带有稀疏值理论的分类神经网络与回归的使用来探讨监督式学习。 - The distinct difference between supervised learning and unsupervised learning lies in whether the example consists of the pre - processed output value
这两种方法最大的区别就在于学习样本是否包含有预先规定好的输出值。 - It's difficult to find supervised learning in a sentence. 用supervised learning造句挺难的
- It also proposes a method of supervised learning to train the decision function and provides the corresponding method of calculation to realize it
提出了一种通过监督学习来训练判别函数的方法，并给出了相应的实现算法。 - Classification is a sort of supervised learning ( i . e . , the learning of the model is " supervised " in that it is told to which class each training sample belongs )
需要指出的是：分类是一种有指导的学习（即模型的学习在被告知每个训练样本属于哪个类的“指导”下进行） 。 - Experiments show that it can acquire lexical items with high frequency effectively and efficiently without the support of the dictionary and the supervised learning in term of corpus
实验表明：在无需词典支持和利用语料库学习的前提下，该算法能够快速、准确地抽取中文文档中的中、高频词条。 - J . xiao , j . su , g . zhou , and c . tan , “ protein - protein interaction extraction : a supervised learning approach ” , first international symposium on semantic mining in biomedicine ( smbm ) , vol . 148 , 2005
蒋明村， “使用自动化样板建立的蛋白质交互作用验证系统” ，国立成功大学资讯工程学系硕士论文，未出版， 2007 。 - Finally , most supervised learning neural networks train themselves through minimizing mean squared error . but when the neural network models trained in this way are used to do forecasting , the existence of outliers result in great imprecision
最后，大多数监督学习神经网络是通过最小化训练集的均方差来训练网络，而野值的存在导致这种训练的神经网络模型在预测时会产生极大的不精确性。 - It overcomes the limitation in the assumption in other semi - supervised learning algorithms that probabilistic distribution of data is known , and has the strong ability of learning new patterns and correcting errors because of stability and plasticity of the adaptive resonance theory
在该系统中取消了一般半监督学习算法中假定已知数据概率分布的条件限制，利用自适应谐振理论的稳定性和可塑性，使其具有非常强的学习新模式和纠正错误能力。
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