Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
Simple is what you need for efficient and accurate medical image segmentation
Permutation-Free High-Order Interaction Tests
Scalable unsupervised feature selection via weight stability
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes
토론
이 논문과 연결된 포럼 글이 아직 없습니다.