论文标题
用支持向量机和经常性神经网络预测樱花树的开花日期
Predicting Blossom Date of Cherry Tree With Support Vector Machine and Recurrent Neural Network
论文作者
论文摘要
我们的项目探讨了温度与樱花日期之间的关系。通过建模，未来的开花将变得预测，帮助公众计划游览并避免花粉季节。为了预测樱桃树的何时开花的日期可以被视为多类分类问题，因此我们应用了多类支持向量分类器（SVC）和复发性神经网络（RNN），尤其是短期记忆（LSTM），以制定问题。最后，我们评估和比较这些方法的性能，以找出哪种方法可能更适用于现实。
Our project probes the relationship between temperatures and the blossom date of cherry trees. Through modeling, future flowering will become predictive, helping the public plan travels and avoid pollen season. To predict the date when the cherry trees will blossom exactly could be viewed as a multiclass classification problem, so we applied the multi-class Support Vector Classifier (SVC) and Recurrent Neural Network (RNN), particularly Long Short-term Memory (LSTM), to formulate the problem. In the end, we evaluate and compare the performance of these approaches to find out which one might be more applicable in reality.
