Kernels and methods for selecting kernels for use in learning machines
AssigneeHealth Discovery Corporation
InventorsPeter L. Bartlett, Andre Elisseeff, Bernhard Schoelkopf, Olivier Chapelle
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AssigneeHealth Discovery Corporation
InventorsPeter L. Bartlett, Andre Elisseeff, Bernhard Schoelkopf, Olivier Chapelle
Estimating the term…
Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets possesses structural characteristics, locational kernels can be utilized to provide measures of similarity among data points within the dataset. The locational kernels are then combined to generate a decision function, or kernel, that can be used to analyze the dataset. Where an invariance transformation or noise is present, tangent vectors are defined to identify relationships between the invariance or noise and the data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel.
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A computer-implemented method for analyzing data comprising a text document to identify patterns in words or characters within the document, the method comprising:
A computer-implemented method for analyzing data comprising a text document to identify patterns in words or characters within the document, the method comprising:
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