Difference between revisions of "Kernel Trick"
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* [[Principal Component Analysis (PCA)]] | * [[Principal Component Analysis (PCA)]] | ||
* [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | * [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] | ||
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* [[Isomap]] | * [[Isomap]] | ||
* [[Local Linear Embedding]] | * [[Local Linear Embedding]] | ||
| + | * [http://en.wikipedia.org/wiki/Kernel_method Kernel method | Wikipedia] | ||
| + | functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in [[Support Vector Machine (SVM)]]. The feature functions perform non-linear transformations of the input, which can serve as a basis for linear classification or other algorithms. The advantage of using approximate explicit feature maps compared to the kernel trick, which makes use of feature maps implicitly, is that explicit mappings can be better suited for online learning and can significantly reduce the cost of learning with very large datasets. Standard kernelized [[Support Vector Machine (SVM)]]s do not scale well to large datasets, but using an approximate kernel map it is possible to use much more efficient linear [[Support Vector Machine (SVM)]]s. [http://scikit-learn.org/stable/modules/kernel_approximation.html Kernel Approximation | Scikit-Learn] | ||
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Revision as of 17:09, 7 January 2019
YouTube search... ...Google search
- AI Solver
- ...find outliers
- Anomaly Detection
- Dimensional Reduction Algorithms
- Principal Component Analysis (PCA)
- T-Distributed Stochastic Neighbor Embedding (t-SNE)
- Isomap
- Local Linear Embedding
- Kernel method | Wikipedia
functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in Support Vector Machine (SVM). The feature functions perform non-linear transformations of the input, which can serve as a basis for linear classification or other algorithms. The advantage of using approximate explicit feature maps compared to the kernel trick, which makes use of feature maps implicitly, is that explicit mappings can be better suited for online learning and can significantly reduce the cost of learning with very large datasets. Standard kernelized Support Vector Machine (SVM)s do not scale well to large datasets, but using an approximate kernel map it is possible to use much more efficient linear Support Vector Machine (SVM)s. Kernel Approximation | Scikit-Learn
