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AssigneeHealth Discovery Corporation
InventorsJason Aaron Edward Weston, André Elisseeff, Bernhard Schoelkopf, Fernando Pérez-Cruz
Estimating the term…
In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (lo-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score and transductive feature selection. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection. (FIG. 3, 300, 301, 302, 304, 306, 308, 309, 310, 311, 312, 314)
A computer-implemented method for identifying a pattern within a large dataset, wherein data points in the dataset correspond to a physical measurement and have a plurality of features that describe attributes of the data point, the method comprising:
A computer-implemented method for identifying a pattern within a large dataset, wherein the data points in the dataset correspond to physical measurements and each data point has a plurality of features that describe attributes of the data point, the method comprising:
Dependencies are inferred from the claim text.