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In machine learning, manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset. In many machine learning problems, the data to be learned do not cover the entire input space. For example, a facial recognition system may not need to classify any possible image, but only the…
Applications, Manifold regularizer & Software
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regularization manifold data displaystyle function kernel learning norm technique space tikhonov laplacian points using learned vector intrinsic labels algorithm algorithms
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Manifold regularization | is a | technique for using the shape of a dataset to constrain the functions that should be learned on that dataset | 0.90 | text |
| Manifold regularization | has application | Manifold | 0.60 | section |
| Manifold regularization | has application | Tikhonov | 0.60 | section |
| Manifold regularization | has application | Two | 0.60 | section |
| Manifold regularization | has application | Regularized | 0.60 | section |
| Manifold regularization | has application | LASSO | 0.60 | section |
| Manifold regularization | has application | The | 0.60 | section |
| Manifold regularization | has application | Laplacian Regularized Least Squares | 0.60 | section |
| Manifold regularization | has application | LapRLS | 0.60 | section |
| Manifold regularization | has application | Laplacian Support Vector Machines | 0.60 | section |
| Manifold regularization | has application | LapSVM | 0.60 | section |
| Manifold regularization | related to Limitations | Manifold | 0.60 | section |
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