Name
Abstract | ||
|---|---|---|
Support vector machines (SVMs) are supervised learning models traditionally employed for classification and regression analysis. In classification analysis, a set of training data is chosen, and each instance in the training data is assigned a categorical class. An SVM then constructs a model based on a separating plane that maximizes the margin between different classes. Despite being one of the most popular classification models because of its strong performance empirically, understanding the knowledge captured in an SVM remains difficult. SVMs are typically applied in a black-box manner where the details of parameter tuning, training, and even the final constructed model are hidden from the users. This is natural since these details are often complex and difficult to understand without proper visualization tools. However, such an approach often brings about various problems including trial-and-error tuning and suspicious users who are forced to trust these models blindly. |
Year | Venue | Keywords |
|---|---|---|
2017 | Computational Visual Media | support vector machines (SVMs), rule extraction, visual classification, high-dimensional visualization, visual analysis |
Field | DocType | Volume |
Data mining,Regression analysis,Computer science,White box,Categorical variable,Artificial intelligence,Training set,Pattern recognition,Least squares support vector machine,Visualization,Support vector machine,Supervised learning,Machine learning | Journal | 3 |
Issue | Citations | PageRank |
2 | 4 | 0.37 |
References | Authors | |
23 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Xin Ma | 1 | 113 | 5.71 |
| Wei Chen | 2 | 1193 | 92.00 |
| Xiaohong Ma | 3 | 4 | 0.71 |
| Jiayi Xu | 4 | 51 | 5.09 |
| Xinxin Huang | 5 | 37 | 1.88 |
| Ross Maciejewski | 6 | 149 | 18.52 |
| Anthony K. H. Tung | 7 | 3263 | 189.90 |