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AdaptiveCutoffNode Node which uses the data history during training to learn cutoff values. |
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CuBICANode Perform Independent Component Analysis using the CuBICA algorithm. |
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CutoffNode Node to cut off values at specified bounds. |
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EtaComputerNode Compute the eta values of the normalized training data. |
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FANode Perform Factor Analysis. |
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FDANode Perform a (generalized) Fisher Discriminant Analysis of its input. |
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FastICANode Perform Independent Component Analysis using the FastICA algorithm. |
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GaussianClassifierNode Perform a supervised Gaussian classification. |
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GrowingNeuralGasNode Learn the topological structure of the input data by building a corresponding graph approximation. |
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HLLENode Perform a Hessian Locally Linear Embedding analysis on the data. |
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HistogramNode Node which stores a history of the data during its training phase. |
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HitParadeNode Collect the first 'n' local maxima and minima of the training signal which are separated by a minimum gap 'd'. |
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ICANode ICANode is a general class to handle different batch-mode algorithm for Independent Component Analysis. |
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ISFANode Perform Independent Slow Feature Analysis on the input data. |
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IdentityNode Return input data (useful in complex network layouts) |
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JADENode Perform Independent Component Analysis using the JADE algorithm. |
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LLENode Perform a Locally Linear Embedding analysis on the data. |
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LibSVMNode Problems with LibSVM:... |
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LinearRegressionNode Compute least-square, multivariate linear regression on the input data, i.e., learn coefficients b_j so that |
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NIPALSNode Perform Principal Component Analysis using the NIPALS algorithm. |
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NoiseNode Inject multiplicative or additive noise into the input data. |
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NormalNoiseNode Special version of NoiseNode for Gaussian additive noise. |
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PCANode Filter the input data through the most significatives of its principal components. |
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PolynomialExpansionNode Perform expansion in a polynomial space. |
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QuadraticExpansionNode Perform expansion in the space formed by all linear and quadratic monomials. |
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RBFExpansionNode Expand input space with Gaussian Radial Basis Functions (RBFs). |
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RBMNode Restricted Boltzmann Machine node. |
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RBMWithLabelsNode Restricted Boltzmann Machine with softmax labels. |
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SFA2Node Get an input signal, expand it in the space of inhomogeneous polynomials of degree 2 and extract its slowly varying components. |
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SFANode Extract the slowly varying components from the input data. |
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TimeFramesNode Copy delayed version of the input signal on the space dimensions. |
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WhiteningNode 'Whiten' the input data by filtering it through the most significatives of its principal components. |
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XSFANode Perform Non-linear Blind Source Separation using Slow Feature Analysis. |
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_OneDimensionalHitParade Class to produce hit-parades (i.e., a list of the largest and smallest values) out of a one-dimensional time-series. |
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Return the size of a vector of dimension 'nvariables' after a polynomial expansion of degree 'degree'. |
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