论文标题
深层网络合奏学习应用于使用CNN树的图像分类
Deep Network Ensemble Learning applied to Image Classification using CNN Trees
论文作者
论文摘要
在处理复杂数据时，传统的机器学习方法可能无法令人满意。在这种情况下，数据挖掘的重要性发展了W.R.T.建立有效的知识发现和采矿框架。合奏学习的目的是将数据的融合，建模和挖掘到统一模型中。但是，传统的合奏学习方法很复杂，并且有优化或调整问题。在本文中，我们提出了一种使用多个深网的简单，顺序，高效，集合学习方法。合奏中使用的深网是RESNET50。该模型从二进制决策/分类树中汲取灵感。将提出的方法与基线相比。单个分类器方法，即在ImageNet和自然图像数据集上使用单个多类Resnet50。我们的方法表现优于Imagenet数据集上所有实验的基线。代码可在https://github.com/mueedhafiz1982/cnntreeensember.git中找到
Traditional machine learning approaches may fail to perform satisfactorily when dealing with complex data. In this context, the importance of data mining evolves w.r.t. building an efficient knowledge discovery and mining framework. Ensemble learning is aimed at integration of fusion, modeling and mining of data into a unified model. However, traditional ensemble learning methods are complex and have optimization or tuning problems. In this paper, we propose a simple, sequential, efficient, ensemble learning approach using multiple deep networks. The deep network used in the ensembles is ResNet50. The model draws inspiration from binary decision/classification trees. The proposed approach is compared against the baseline viz. the single classifier approach i.e. using a single multiclass ResNet50 on the ImageNet and Natural Images datasets. Our approach outperforms the baseline on all experiments on the ImageNet dataset. Code is available in https://github.com/mueedhafiz1982/CNNTreeEnsemble.git
