Inorganic Phosphates Investigation by Support Vector Machine

机译：支持向量机研究无机磷酸盐

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We dealt the prediction of crystal chemical features of new sinthesized inorganic phosphates with a supervised-learning regression problem. Then, we analysed correlations between crystal chemical properties of phosphate crystals by a learning machine method, Support Vector Machine (SVM), to develop the detection algorithm. Using structural properties of phosphate crystal structures widely described in the literature, we developed several SVMs able to capture statistical relations between crystal chemical properties of the anhydrous phosphates from the available dataset. In this way, we demonstrated the suitability of SVM for the prediction of structural properties of crystals.

机译：我们用监督学习回归问题处理了新的合成无机磷酸盐的晶体化学特征的预测。然后，我们通过学习机方法，支持向量机（SVM）分析了磷酸盐晶体的晶体化学性质之间的相关性，从而开发了检测算法。使用在文献中广泛描述的磷酸盐晶体结构的结构特性，我们开发了几种SVM，它们能够从可用数据集中捕获无水磷酸盐的晶体化学特性之间的统计关系。通过这种方式，我们证明了SVM在预测晶体结构特性方面的适用性。

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《International Conference on Computational Science and Its Applications(ICCSA 2004) pt.2; 20040514-20040517; Assisi; IT》|2004年|P.338-349|共12页
会议地点 Assisi(IT);Assisi(IT)
作者
Cinzia Pierro; Francesco Capitelli;
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作者单位

Center of Innovative Technologies for Signal Detection and Processing (TIRES), c/o Department of Physics, University of Bari, Via Orabona 4, 70125 Bari, Italy;

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原文格式 PDF
正文语种 eng
中图分类理论、方法;
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Inorganic Phosphates Investigation by Support Vector Machine

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