Pre-processing input data with outlier values for a support vector machine
First Claim
1. A data preprocessor for preprocessing input data for a support vector machine, wherein the input data include one or more outlier values, comprising:
- an input buffer which is operable to receive and store the input data wherein the input data comprise run-time data;
a data filter which is operable to detect and remove said one or more outlier values, thereby generating corrected input data, wherein said corrected input data comprise corrected run-time data; and
an output device for outputting the corrected input data, said corrected input data comprising the input data to the support vector machine;
wherein the support vector machine comprises a non-linear model having a set of model parameters defining a representation of a system, wherein said model parameters of said support vector machine have been trained to represent said system; and
wherein the support vector machine is operable to receive said corrected run-time data and generate run-time output data, wherein said run-time output data comprise one or both of control parameters for said system and predictive output information for said system.
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Abstract
A system and method for preprocessing input data to a support vector machine (SVM). The SVM is a system model having parameters that define the representation of the system being modeled, and operates in two modes: run-time and training. A data preprocessor preprocesses received data in accordance with predetermined preprocessing parameters, and outputs preprocessed data. The data preprocessor includes an input buffer for receiving and storing the input data. The input data may include one or more outlier values. A data filter detects and removes any outlier values in the input data, generating corrected input data. The filter may optionally replace the outlier values in the input data. An output device outputs the corrected data from the data filter as preprocessed data. The corrected data may be input to the SVM in training mode to train the SVM, and/or in run-time mode to generate control parameters and/or predictive output information.
61 Citations
36 Claims
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1. A data preprocessor for preprocessing input data for a support vector machine, wherein the input data include one or more outlier values, comprising:
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an input buffer which is operable to receive and store the input data wherein the input data comprise run-time data;
a data filter which is operable to detect and remove said one or more outlier values, thereby generating corrected input data, wherein said corrected input data comprise corrected run-time data; and
an output device for outputting the corrected input data, said corrected input data comprising the input data to the support vector machine;
wherein the support vector machine comprises a non-linear model having a set of model parameters defining a representation of a system, wherein said model parameters of said support vector machine have been trained to represent said system; and
wherein the support vector machine is operable to receive said corrected run-time data and generate run-time output data, wherein said run-time output data comprise one or both of control parameters for said system and predictive output information for said system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system for preprocessing input data for a support vector machine having multiple inputs, each of the inputs associated with a portion of the input data, wherein the input data include one or more outlier values, comprising:
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means for receiving and storing the input data, wherein the input data comprise run-time data;
means for analyzing said input data to determine said one or more outliers values;
means for removing said one or more outlier values, thereby generating corrected input data, wherein said corrected input data comprise corrected run-time data; and
means for outputting the corrected input data, said corrected input data comprising the input data to the support vector machine;
wherein the support vector machine comprises a non-linear model having a set of model parameters defining a representation of a system, wherein said model parameters of said support vector machine have been trained to represent said system, the system further comprising;
means for inputting said corrected run-time data into the support vector machine to generate run-time output data, wherein said run-time output data comprise one or both of control parameters for said system and predictive output information for said system. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A carrier medium which stores program instructions for preprocessing input data prior to input to a support vector machine having multiple inputs, each of the inputs associated with a portion of the input data, wherein the input data include one or more outlier values, wherein said program instructions are executable to:
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receive and store the input data, wherein the input data comprise run-time data;
analyze said input data to determine said one or more outliers values;
remove said one or more outlier values, thereby generating corrected input data2 wherein said corrected input data comprise corrected run-time data; and
output the corrected input data, said corrected input data comprising the input data to the support vector machine, wherein the support vector machine comprises a non-linear model having a set of model parameters defining a representation of a system, wherein said model parameters of said support vector machine have been trained to represent said system, wherein said program instructions are further executable to;
input said corrected run-time data into the support vector machine to generate run-time output data, wherein said run-time output data comprise one or both of control parameters for said system and predictive output information for said system. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26, 27)
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28. A method for preprocessing input data prior to input to a support vector machine having multiple inputs, each of the inputs associated with a portion of the input data, wherein the input data include one or more outlier values, the method comprising:
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receiving and storing the input data wherein the input data comprise run-time data;
analyzing said input data to determine said one or more outliers values;
removing said one or more outlier values, thereby generating corrected input data1 wherein said corrected input data comprise corrected run-time data; and
outputting the corrected input data, said corrected input data comprising the input data to the support vector machine;
wherein the support vector machine comprises a non-linear model having a set of model parameters defining a representation of a system, wherein said model parameters of said support vector machine have been trained to represent said system, the method further comprising;
inputting said corrected run-time data into the support vector machine to generate run-time output data, wherein said run-time output data comprise one or both of control parameters for said system and predictive output information for said system. - View Dependent Claims (29, 30, 31, 32, 33, 34, 35, 36)
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Specification