Name
Abstract | ||
|---|---|---|
Most traditional supervised learning methods are developed to learn a model from labeled examples and use this model to classify the unlabeled ones into the same label space predefined by the models. However, in many real world applications, the label spaces for both the labeled/training and unlabeled/testing examples can be different. To solve this problem, this paper proposes a novel notion of Serendipitous Learning (SL), which is defined to address the learning scenarios in which the label space can be enlarged during the testing phase. In particular, a large margin approach is proposed to solve SL. The basic idea is to leverage the knowledge in the labeled examples to help identify novel/unknown classes, and the large margin formulation is proposed to incorporate both the classification loss on the examples within the known categories, as well as the clustering loss on the examples in unknown categories. An efficient optimization algorithm based on CCCP and the bundle method is proposed to solve the optimization problem of the large margin formulation of SL. Moreover, an efficient online learning method is proposed to address the issue of large scale data in online learning scenario, which has been shown to have a guaranteed learning regret. An extensive set of experimental results on two synthetic datasets and two datasets from real world applications demonstrate the advantages of the proposed method over several other baseline algorithms. One limitation of the proposed method is that the number of unknown classes is given in advance. It may be possible to remove this constraint if we model it by using a non-parametric way. We also plan to do experiments on more real world applications in the future. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2020408.2020608 | KDD |
Keywords | Field | DocType |
predefined label space,unknown class,unknown category,large scale data,bundle method,traditional supervised learning method,label space,large margin formulation,large margin approach,real world application,supervised learning,optimization problem | Online machine learning,Data mining,Stability (learning theory),Semi-supervised learning,Active learning (machine learning),Computer science,Supervised learning,Unsupervised learning,Artificial intelligence,Cluster analysis,Optimization problem,Machine learning | Conference |
Citations | PageRank | References |
6 | 0.46 | 18 |
Authors | ||
3 | ||