Name
Abstract | ||
|---|---|---|
Graph-based learning provides a useful approach for modeling data in classification problems. In this modeling scenario, the relationship between labeled and unlabeled data impacts the construction and performance of classifiers, and therefore a semi-supervised learning framework is adopted. We propose a graph classifier based on kernel smoothing. A regularization framework is also introduced, and it is shown that the proposed classifier optimizes certain loss functions. Its performance is assessed on several synthetic and real benchmark data sets with good results, especially in settings where only a small fraction of the data are labeled. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TPAMI.2007.70765 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
semi-supervised learning framework,regularization framework,graph-based semisupervised learning,unlabeled data,classification problem,proposed classifier,graph classifier,graph-based learning,real benchmark data set,modeling scenario,certain loss function,matrix decomposition,semi supervised learning,kernel,learning artificial intelligence,graph theory,machine learning,nonparametric statistics,labeling,constraint optimization,kernel smoothing,optimization,loss function,clustering algorithms | Graph theory,Data modeling,Data set,Semi-supervised learning,Pattern recognition,Computer science,Smoothing,Artificial intelligence,Cluster analysis,Classifier (linguistics),Kernel method,Machine learning | Journal |
Volume | Issue | ISSN |
30 | 1 | 0162-8828 |
Citations | PageRank | References |
48 | 1.54 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Culp | 1 | 87 | 4.57 |
| George Michailidis | 2 | 303 | 35.19 |