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Found 5 papers, 0 papers with code
Classification via Tensor Decompositions of Echo State Networks
This work proposes a tensor-based supervised classification method on echo state network data that preserves and exploits the multidimensional nature of the hidden layer states.
Comparison of echo state network output layer classification methods on noisy data
A strength of echo state networks is the simple method used to train the output layer - typically a collection of linear readout weights found using a least squares approach.
Reservoir computing for spatiotemporal signal classification without trained output weights
Reservoir computing is a recently introduced machine learning paradigm that has been shown to be well-suited for the processing of spatiotemporal data.
Finding Dantzig selectors with a proximity operator based fixed-point algorithm
In this paper, we study a simple iterative method for finding the Dantzig selector, which was designed for linear regression problems.
Separation of undersampled composite signals using the Dantzig selector with overcomplete dictionaries
In this paper, we propose using the Dantzig selector model incorporating an overcomplete dictionary to separate a noisy undersampled collection of composite signals, and present an algorithm to efficiently solve the model.
