Name
Abstract | ||
|---|---|---|
In this paper we consider a new paradigm of learning: learning using hidden information. The classical paradigm of the supervised learning is to learn a decision rule from labeled data (xi, yi), xi ∈ X, xi ∈ X, yi ∈ {-1, 1}, i = 1, ..., l. In this paper we consider a new setting: given training vectors in space X along with labels and description of this data in another space X*, find in space X a decision rule better than the one found in the classical paradigm. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IJCNN.2009.5178760 | IJCNN |
Keywords | Field | DocType |
decision rule,new paradigm,space x,new setting,classical paradigm,supervised learning,hidden information,training vector,neural networks,support vector machines,pixel,convergence,machine learning,learning artificial intelligence,testing,proteins,national electric code,training data,kernel | Algorithmic learning theory,Semi-supervised learning,Instance-based learning,Stability (learning theory),Pattern recognition,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Preference learning,Computational learning theory,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 18 | 1.21 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Vapnik | 1 | 16075 | 3397.91 |
| Akshay Vashist | 2 | 176 | 12.64 |
| Natalya Pavlovitch | 3 | 19 | 1.72 |