基于Naive Bayes Classifiers的航空影像纹理分类

By 虞欣, 郑肇葆, 汤凌, 叶志伟. 2006

505 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5a03a6aaf42e07854de6c1e95e9a366dfb62ccfb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Paulo Valente Klaine
2. Richard Demo Souza
3. Oluwakayode Onireti
4. Muhammad Ali Imran
5. Mingzhe Chen
6. Chaoyun Zhang
7. Zhiguo Ding

Citations in the corpus

• Detection (2020, unknown): “In order to solve the supervised binary classification problem, we resort to multiple ML classifiers: k-Nearest-Neighbors (kNN) [26], Gaussian Naive Bayes (GNB) [27], decision tree (DT) [28], random forest (RF) [29], and eX-treme Gradient Boosting (XGB) [30].”
• A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks (2017, background): “For interested readers, a more in-depth review of Bayesian classifiers, its advantages and disadvantages, and its two models, can be found in [46], [47].”