Searching for "On Learning Vector-Valued Functions." – sorted by Relevance.
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Recovering Patch Parameters from the Optic Flow with Auto Associative Neural Networks
- MLP are general function approximators, we could learn the vector valued function F(s). The problem
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Learning Structure from Motion: How to Represent Two-Valued Functions
- approximators, we could learn the vector valued function l'(s). The problem is that there are no learning
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SINGLE IMAGE SUPERRESOLUTION BASED ON SUPPORT VECTOR REGRESSION
- . Recently, there has been some work on learning vector valued function using operator-valued kernel [4
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Convex multi-task feature learning
- , Inductive Transfer, Kernels, Multi- Task Learning, Regularization, Transfer Learning, Vector-Valued
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IBMG: Interpretable Behavioral Model Generator for Nonlinear Analog Circuits via
- ,0,…). IBMG must “learn” the vector valued function g(z, u) as well as E and F. Learning E and F is merely a
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