Abstract
Purchase PDF (3372 K)
E-mail Article
Add to my Quick Links
Cited By in Scopus (1)
Purchase PDF (890 K)
doi:10.1016/j.neucom.2006.10.149 
Copyright © 2007 Elsevier B.V. All rights reserved.
Margin-based active learning for LVQ networks
Available online 22 December 2006.
References and further reading may be available for this article. To view references and further reading you must purchase this article.
Abstract
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation procedure. The proposed active learning strategy aims on an improved generalization ability of the final model. This is achieved by usage of an adaptive query strategy which is more adequate for supervised learning than a simple random approach. Beside an improved generalization ability the method also improves the speed of the learning procedure which is especially beneficial for large data sets with multiple similar items. The algorithm is based on the idea of selecting a query on the borderline of the actual classification. This can be done by considering margins in an extension of learning vector quantization based on an appropriate cost function. The proposed active learning approach is analyzed for two kinds of learning vector quantizers the supervised relevance neural gas and the supervised nearest prototype classifier, but is applicable for a broader set of prototype-based learning approaches too. The performance of the query algorithm is demonstrated on synthetic and real life data taken from clinical proteomic studies. From the proteomic studies high-dimensional mass spectrometry measurements were calculated which are believed to contain features discriminating the different classes. Using the proposed active learning strategies, the generalization ability of the models could be kept or improved accompanied by a significantly improved learning speed. Both of these characteristics are important for the generation of predictive clinical models and were used in an initial biomarker discovery study.
Keywords: Active learning; Learning vector quantization; Generalization; Classification; Proteomic profiling
Article Outline
- 1. Introduction
- 2. Soft nearest prototype classification
- 3. Margin-based active learning
- 3.1. Theoretical generalization bound for fixed margin
- 3.2. Active learning strategy
- 3.3. Validity of generalization bounds for active strategies
- 3.4. Adaptation to noisy data or unknown classes
- 4. Synthetic and clinical data sets
- 5. Experiments and results
- 6. Conclusion
- References
- Vitae
