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首页> 外文期刊>Journal of chemical information and modeling >Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients
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Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients

机译：知识的归纳传递：多任务学习和特征网方法在组织空气分配系数模型中的应用

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摘要

Two inductive knowledge transfer approaches - multitask learning (MTL) and Feature Net (FN) - have been used to build predictive neural networks (ASNN) and PLS models for I I types of tissue-air partition coefficients (TAPC). Unlike conventional single-task learning (STL) modeling focused only on a single target property without any relations to other properties, in the framework of inductive transfer approach, the individual models are viewed as nodes in the network of interrelated models built in parallel (MTL) or sequentially (FN). It has been demonstrated that MTL and FN techniques are extremely useful in structure-property modeling on small and structurally diverse data sets, when conventional STL modeling is unable to produce any predictive model. The predictive STL individual models were obtained for 4 out of I I TAPC, whereas application of inductive knowledge transfer techniques resulted in models for 9 TAPC. Differences in prediction performances of the models as a function of the machine-learning method, and of the number of properties simultaneously involved in the learning, has been discussed.
机译：两种归纳性知识转移方法-多任务学习（MTL）和特征网（FN）-已被用来为组织类型的空气分配系数（TAPC）建立预测神经网络（ASNN）和PLS模型。与传统的单任务学习（STL）建模仅关注单个目标属性而与其他属性没有任何关系不同，在归纳传递方法的框架中，单个模型被视为并行构建的相互关联模型网络（MTL）中的节点）或按顺序（FN）。已经证明，当传统的STL建模无法产生任何预测模型时，MTL和FN技术在基于小型且结构多样的数据集的结构属性建模中非常有用。对于I TAPC中的4个，获得了预测性STL个体模型，而归纳知识转移技术的应用导致了9 TAPC的模型。讨论了模型的预测性能与机器学习方法以及学习中同时涉及的属性数量之间的差异。

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