论文标题
一种基于索引的新型多维数据组织模型，可增强机器学习算法的可预测性
A Novel index-based multidimensional data organization model that enhances the predictability of the machine learning algorithms
论文作者
论文摘要
从多维数据中学习一直是机器学习领域的一个有趣概念。但是，由于昂贵的数据处理，这种学习可能是困难，复杂，昂贵的，随着维度数量的增加而操作。结果，我们引入了一个有序的基于索引的数据组织模型，因为有序的数据集比无序的数据集提供了易于有效的访问，最后，该组织可以改善学习。排序映射减少空间中的多维数据集，并确保可以有效地来回检索与学习相关的信息。我们发现，这种多维数据存储可以增强无监督和监督机器学习算法的可预测性。
Learning from the multidimensional data has been an interesting concept in the field of machine learning. However, such learning can be difficult, complex, expensive because of expensive data processing, manipulations as the number of dimension increases. As a result, we have introduced an ordered index-based data organization model as the ordered data set provides easy and efficient access than the unordered one and finally, such organization can improve the learning. The ordering maps the multidimensional dataset in the reduced space and ensures that the information associated with the learning can be retrieved back and forth efficiently. We have found that such multidimensional data storage can enhance the predictability for both the unsupervised and supervised machine learning algorithms.
