论文标题
基于计算的低成本同质机器学习模型算法的性能评估和应用图像分类
Performance evaluation and application of computation based low-cost homogeneous machine learning model algorithm for image classification
论文作者
论文摘要
图像分类机器学习模型的训练旨在预测输入图像的类别。尽管公开可用多种最先进的合奏模型方法，但本文评估了一种低成本，简单的算法的性能，该算法将无缝集成到现代生产级的基于云的应用程序中。均匀的模型，用完整的数据训练，而不是数据子集，其中包含不同的超参数和神经层。这些模型的推论将由新算法处理，该算法基于条件概率理论而松散。最终输出将进行评估。
The image classification machine learning model was trained with the intention to predict the category of the input image. While multiple state-of-the-art ensemble model methodologies are openly available, this paper evaluates the performance of a low-cost, simple algorithm that would integrate seamlessly into modern production-grade cloud-based applications. The homogeneous models, trained with the full instead of subsets of data, contains varying hyper-parameters and neural layers from one another. These models' inferences will be processed by the new algorithm, which is loosely based on conditional probability theories. The final output will be evaluated.
