• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Statistics and Probability >Boosted Convolutional Decision Trees for Translationally Invariant Pattern Recognition and Transfer Learning
【24h】

Boosted Convolutional Decision Trees for Translationally Invariant Pattern Recognition and Transfer Learning

机译：用于翻译不变模式识别和转移学习的卷积卷积决策树

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Decision Tree (DT) models provide a well-known class of interpretable machine learning tools for diverse pattern recognition problems. However, applying DTs to learn floating features in images and categorical data based on their raw representation has been challenging. Convolutional Neural Networks (CNNs) are the current state-of-the-art method for classifying raw images, but have their own disadvantages, including that they are often difficult to interpret, have a large number of parameters and hyperparameters, require a fixed image size, and have only partial translational invariance directly built into its architecture. We propose a novel application of Convolutional Decision Trees (CDTs) and show that our approach is more interpretable and can learn higher quality convolutional filters compared to CNNs. CDTs have full translational invariance built into the architecture and can be trained and make predictions on variable-sized images. Using two independent test casesprotein-DNA binding prediction, and hand-written digit classificationwe demonstrate that our GPU-enabled implementation of the Cross Entropy (CE) optimization method for training CDTs learns informative convolutional filters that can both facilitate accurate data classifications in a tree-like pattern and be used for transfer learning to improve CNNs themselves. These results motivate further studies on developing accurate and efficient tree-based models for pattern recognition and computer vision.
机译：决策树（DT）模型为各种模式识别问题提供了一个已知的可解释的机器学习工具。但是，应用DTS以基于其原始表示的图像和分类数据中的浮动特征一直在具有挑战性。卷积神经网络（CNNS）是用于分类原始图像的当前最先进的方法，但具有自己的缺点，包括它们通常难以解释，具有大量参数和超参数，需要一个固定的图像大小，只有局部平移的不变性直接内置于其架构中。我们提出了一种新颖的应用卷积决策树（CDTS），并表明我们的方法更具可解释，并且与CNN相比，可以学习更高质量的卷积滤波器。 CDTS内置于体系结构中的完全翻译不变性，可以在可变尺寸的图像上进行培训并进行预测。使用两个独立的测试用例括号 - DNA绑定预测，手写的数字分类我们证明了我们的GPU的跨熵实现（CE）优化方法用于训练CDTS学习信息卷积滤波器，可以促进树中的准确数据分类 - 像图案一样，用于转移学习，以改善CNNS本身。这些结果可以进一步研究为模式识别和计算机视觉制定准确和有效的树木模型。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号