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Related Experiment Videos

Comparing support vector machines and feedforward neural networks with similar hidden-layer weights.

Enrique Romero, Daniel Toppo

IEEE Transactions on Neural Networks

|May 29, 2007

Summary

Support Vector Machines (SVMs) and sequential Feedforward Neural Networks (FNNs) show similar accuracy. Sequential FNNs create sparser models with fewer hidden units compared to standard SVMs, though SVMs are faster computationally.

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Area of Science:

Machine Learning
Artificial Intelligence
Computational Science

Background:

Support Vector Machines (SVMs) typically require numerous support vectors for output generation.
Recent advancements aim to create SVMs with fewer basis functions while retaining support vector properties.
Sequential Feedforward Neural Networks (FNNs) also exhibit sparse model properties with controllable hidden units.

Purpose of the Study:

To compare the performance of standard Support Vector Machines (SVMs) against sequential Feedforward Neural Networks (FNNs).
To evaluate both models under identical conditions, focusing on similar hidden-layer weight constraints.
To analyze model sparsity, accuracy, and computational efficiency.

Main Methods:

An experimental study was conducted on multiple benchmark datasets.
Support Vector Machines (SVMs) and sequential Feedforward Neural Networks (FNNs) were implemented.
Models were trained and evaluated under consistent experimental conditions.

Main Results:

Accuracy outcomes for both SVMs and sequential FNNs were found to be highly comparable.
Sequential FNNs generated models with fewer hidden units than standard SVMs, aligning with 'sparse' SVMs.
SVMs demonstrated lower computational times compared to the sequential FNNs.

Conclusions:

Sequential FNNs offer a viable alternative to SVMs, achieving similar accuracy with enhanced model sparsity.
The choice between SVMs and sequential FNNs may depend on the trade-off between computational speed and model size.
Both approaches, when constrained to similar hidden-layer weights, present competitive performance characteristics.