用编码解码框架改进神经网络回归训练效果
Building Bridges between Regression, Clustering, and Classification
- 将回归转为编码-解码结构，借鉴分类聚类思路
- 在多个真实数据集上显著提升回归性能
- 适合需要高精度回归的工程与科研场景
回归是基于特征x预测连续标量目标y的基础机器学习任务。已有观察和理论分析表明，经典的均方误差最小化方法在训练神经网络时可能导致次优结果。本文提出一种新方法，通过引入目标编码器与预测解码器，以不同方式重构回归任务，受分类与聚类方法启发。该方法在多种真实世界数据集上展现出优异性能。
原文摘要 · Abstract (English)
Regression, the task of predicting a continuous scalar target y based on some features x is one of the most fundamental tasks in machine learning and statistics. It has been observed and theoretically analyzed that the classical approach, meansquared error minimization, can lead to suboptimal results when training neural networks. In this work, we propose a new method to improve the training of these models on regression tasks, with continuous scalar targets. Our method is based on casting this task in a different fashion, using a target encoder, and a prediction decoder, inspired by approaches in classification and clustering. We showcase the performance of our method on a wide range of real-world datasets.
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