Multiple order modeling for deep learning

This is an example of multiple order modeling for accuracy improvement in deep neural networks. Different approaches are shown on how to use

https://github.com/muquitMW/multiple_order_modeling

版本 1.1 (334.7 KB)
101.0 次下载
(0)
2020/8/13

您现在正在关注此提交

Introduction
This is an example of multiple order modeling for accuracy improvement in deep neural networks.
Different approaches are shown on how to use the outputs of a category prediction model as predictors for a second model.
Time series instances of samples are used as multiple inputs (for example N frames of a video is used as N image inputs) for model,
and those N number of predicted output (probability density) is used as predictors for the second model.

Data
We attach a set of simulated data for testing this approach.
Details regarding the data is available in comment section of FE_DataLoad.m

Supporting function
A function (trainClassifier.m) attached here for training data with SVM algorithm is called by the scripts.
This function is generated using MATLAB's CalssifierLearner App's code generation functionality

引用格式

Mohammad (2026). Multiple order modeling for deep learning (https://github.com/muquitMW/multiple_order_modeling/releases/tag/1.1), GitHub. 检索时间: .

标签

添加标签

Add the first tag.

一般信息

MATLAB 版本兼容性

  • 兼容任何版本

平台兼容性

  • Windows
  • macOS
  • Linux
在新标签页中打开
版本 已发布 发行说明
1.1

See release notes for this release on GitHub: https://github.com/muquitMW/multiple_order_modeling/releases/tag/1.1
要查看或报告此来自 GitHub 的附加功能中的问题，请访问其 GitHub 存储库。
要查看或报告此来自 GitHub 的附加功能中的问题，请访问其 GitHub 存储库。