A Parallel Way to Select the Parameters of SVM Based on the Ant Optimization Algorithm
원문 초록
A large number of experimental data shows that Support Vector Machine (SVM) algorithm has obvious advantages in text classification, handwriting recognition, image classification, bioinformatics, and some other fields. To some degree, the optimization of SVM depends on its kernel function and Slack variable, the determinant of which is its parameters $\delta$ and c in the classification function. That is to say,to optimize the SVM algorithm, the optimization of the two parameters play a huge role. Ant Colony Optimization (ACO) is optimization algorithm which simulate ants to find the optimal path.In the available literature, we mix the ACO algorithm and Parallel algorithm together to find a well parameters.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Image and Text fusion for UPMC Food-101 \\using BERT and CNNs
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
A Hybrid Classification Approach using Topic Modeling and Graph Convolution Networks
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion
토론
이 논문과 연결된 포럼 글이 아직 없습니다.