[Paper Review] Margin-Based Feed-Forward Neural Network Classifiers

Han Xiao, Xiaoyan Zhu|arXiv (Cornell University)|Jun 11, 2015

Neural Networks and Applications8 references3 citations

TL;DR

This paper proposes a novel margin-based training algorithm for feed-forward neural networks that improves generalization and accuracy, especially with sparse labeled data. By applying Min-Margin Principle to feature abstraction and Max-Margin Principle to classification, the method enhances performance over traditional Widrow-Hoff backpropagation with minimal architectural changes.

ABSTRACT

Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labelled samples and flexible network. We have conducted experiments on four UCI open datasets and achieved good results as expected. In conclusion, our model could handle more sparse labelled and more high-dimension dataset in a high accuracy while modification from old ANN method to our method is easy and almost free of work.

Motivation & Objective

• To address the limited generalization ability of traditional feed-forward neural networks trained via Widrow-Hoff Principle.
• To improve performance on datasets with few labeled samples and high dimensionality.
• To integrate margin-based principles into both feature abstraction and classification stages of neural networks.
• To enable seamless migration from standard backpropagation networks to the proposed margin-based framework with near-zero implementation overhead.

Proposed method

• Decomposes the feed-forward neural network into two stages: feature abstraction (input to hidden layers) and classification (hidden to output layers).
• Applies the Min-Margin Principle to the feature abstraction stage to minimize regression error and improve feature representation.
• Applies the Max-Margin Principle to the classification stage to minimize structural risk and enhance decision boundary separation.
• Combines both principles into a unified optimization problem that jointly trains the network using margin-based objectives.
• Develops a novel training algorithm to solve the resulting optimization problem, differing from standard squared-error backpropagation.
• Maintains identical inference and network architecture as standard ANN, enabling drop-in replacement with improved performance.

Experimental results

Research questions

• RQ1 Can margin-based principles improve the generalization and accuracy of feed-forward neural networks compared to Widrow-Hoff backpropagation?
• RQ2 Does applying Min-Margin Principle to the feature abstraction stage enhance representation learning in low-data regimes?
• RQ3 Does applying Max-Margin Principle to the classification stage improve robustness and performance on sparse datasets?
• RQ4 To what extent can the proposed method be integrated into existing neural network codebases with minimal modification?

Key findings

• The proposed margin-based training algorithm significantly outperforms standard backpropagation (ANN) in terms of accuracy, especially on sparse-labeled datasets.
• On the ISOLET dataset with only 3.33% labeled data, the model achieved higher accuracy than ANN across all tested hidden node configurations.
• The model surpassed SVM-RBF on all datasets, demonstrating that its Min-Margin-based abstraction better captures data manifold structure than kernel methods.
• The model also outperformed AdaBoost, confirming the effectiveness of Max-Margin-based classification in handling complex decision boundaries.
• A suitable hidden node count exists where performance peaks, with overfitting occurring at higher node counts and underfitting at lower counts.
• The proposed method enables near-instant migration from standard ANN to margin-based training with no code changes to inference or architecture.