论文标题
通过过度拟合控制
Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control
论文作者
论文摘要
本文使用二进制粒子群优化（BPSO）在手写签名验证（HSV）的背景下进行特征选择时，研究了过度拟合的存在。 Signet是用于HSV上下文中特征表示形式的最先进的CNN模型，并包含2048个维度。这些维度中的一些可能包括由由作者独立的（WI）方法使用的二分法转化（DT）产生的差异表示空间中的冗余信息。该分析是在GPDS-960数据集上进行的。实验表明，所提出的方法能够控制搜索最判别表示的过度拟合。
This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV). SigNet is a state of the art Deep CNN model for feature representation in the HSV context and contains 2048 dimensions. Some of these dimensions may include redundant information in the dissimilarity representation space generated by the dichotomy transformation (DT) used by the writer-independent (WI) approach. The analysis is carried out on the GPDS-960 dataset. Experiments demonstrate that the proposed method is able to control overfitting during the search for the most discriminant representation.
