Patents by Inventor Jason Aaron Edward Weston
Jason Aaron Edward Weston has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10402685Abstract: Identification of a determinative subset of features from within a group of features is performed by training a support vector machine using training samples with class labels to determine a value of each feature, where features are removed based on their the value. One or more features having the smallest values are removed and an updated kernel matrix is generated using the remaining features. The process is repeated until a predetermined number of features remain which are capable of accurately separating the data into different classes. In some embodiments, features are eliminated by a ranking criterion based on a Lagrange multiplier corresponding to each training sample.Type: GrantFiled: November 11, 2010Date of Patent: September 3, 2019Assignee: HEALTH DISCOVERY CORPORATIONInventors: Isabelle Guyon, Jason Aaron Edward Weston
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Patent number: 8463718Abstract: Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.Type: GrantFiled: February 4, 2010Date of Patent: June 11, 2013Assignee: Health Discovery CorporationInventors: Asa Ben-Hur, Andre Elisseeff, Olivier Chapelle, Jason Aaron Edward Weston
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Patent number: 7970718Abstract: A group of features that has been identified as “significant” in being able to separate data into classes is evaluated using a support vector machine which separates the dataset into classes one feature at a time. After separation, an extremal margin value is assigned to each feature based on the distance between the lowest feature value in the first class and the highest feature value in the second class. Separately, extremal margin values are calculated for a normal distribution within a large number of randomly drawn example sets for the two classes to determine the number of examples within the normal distribution that would have a specified extremal margin value. Using p-values calculated for the normal distribution, a desired p-value is selected. The specified extremal margin value corresponding to the selected p-value is compared to the calculated extremal margin values for the group of features.Type: GrantFiled: September 26, 2010Date of Patent: June 28, 2011Assignee: Health Discovery CorporationInventors: Isabelle Guyon, Andre Elisseeff, Bernhard Schoelkopf, Jason Aaron Edward Weston, Fernando Perez-Cruz
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Patent number: 7921068Abstract: The data mining platform comprises a plurality of system modules, each formed from a plurality of components. Each module has an input data component, a data analysis engine for processing the input data, an output data component for outputting the results of the data analysis, and a web server to access and monitor the other modules within the unit and to provide communication to other units. Each module processes a different type of data, for example, a first module processes microarray (gene expression) data while a second module processes biomedical literature on the Internet for information supporting relationships between genes and diseases and gene functionality. In the preferred embodiment, the data analysis engine is a kernel-based learning machine, and in particular, one or more support vector machines (SVMs).Type: GrantFiled: October 30, 2007Date of Patent: April 5, 2011Assignee: Health Discovery CorporationInventors: Isabelle Guyon, Edward P. Reiss, René Doursat, Jason Aaron Edward Weston
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Publication number: 20110078099Abstract: A group of features that has been identified as “significant” in being able to separate data into classes is evaluated using a support vector machine which separates the dataset into classes one feature at a time. After separation, an extremal margin value is assigned to each feature based on the distance between the lowest feature value in the first class and the highest feature value in the second class. Separately, extremal margin values are calculated for a normal distribution within a large number of randomly drawn example sets for the two classes to determine the number of examples within the normal distribution that would have a specified extremal margin value. Using p-values calculated for the normal distribution, a desired p-value is selected. The specified extremal margin value corresponding to the selected p-value is compared to the calculated extremal margin values for the group of features.Type: ApplicationFiled: September 26, 2010Publication date: March 31, 2011Applicant: HEALTH DISCOVERY CORPORATIONInventors: Jason Aaron Edward Weston, André Elisseeff, Bernhard Schöelkopf, Fernando Perez-Cruz, Isabelle Guyon
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Publication number: 20100205124Abstract: Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.Type: ApplicationFiled: February 4, 2010Publication date: August 12, 2010Applicant: HEALTH DISCOVERY CORPORATIONInventors: Asa Ben-Hur, Andre Elisseeff, Olivier Chapelle, Jason Aaron Edward Weston
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Patent number: 7676442Abstract: Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.Type: GrantFiled: October 30, 2007Date of Patent: March 9, 2010Assignee: Health Discovery CorporationInventors: Asa Ben-Hur, André Elisseeff, Olivier Chapelle, Jason Aaron Edward Weston
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Patent number: 7624074Abstract: 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 (l0-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.Type: GrantFiled: October 30, 2007Date of Patent: November 24, 2009Assignee: Health Discovery CorporationInventors: Jason Aaron Edward Weston, Andre′ Elisseeff, Bernard Schoelkopf, Fernando Pérez-Cruz
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Patent number: 7617163Abstract: Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.Type: GrantFiled: October 9, 2002Date of Patent: November 10, 2009Assignee: Health Discovery CorporationInventors: Asa Ben-Hur, André Elisseeff, Olivier Chapelle, Jason Aaron Edward Weston
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Patent number: 7542947Abstract: The data mining platform comprises a plurality of system modules, each formed from a plurality of components. Each module has an input data component, a data analysis engine for processing the input data, an output data component for outputting the results of the data analysis, and a web server to access and monitor the other modules within the unit and to provide communication to other units. Each module processes a different type of data, for example, a first module processes microarray (gene expression) data while a second module processes biomedical literature on the Internet for information supporting relationships between genes and diseases and gene functionality. In the preferred embodiment, the data analysis engine is a kernel-based learning machine, and in particular, one or more support vector machines (SVMs).Type: GrantFiled: October 30, 2007Date of Patent: June 2, 2009Assignee: Health Discovery CorporationInventors: Isabelle Guyon, Edward P. Reiss, René Doursat, Jason Aaron Edward Weston, David D. Lewis
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Patent number: 7444308Abstract: The data mining platform comprises a plurality of system modules (500, 550), each formed from a plurality of components. Each module has an input data component (502, 552), a data analysis engine (504, 554) for processing the input data, an output data component (506, 556) for outputting the results of the data analysis, and a web server (510) to access and monitor the other modules within the unit and to provide communication to other units. Each module processes a different type of data, for example, a first module processes microarray (gene expression) data while a second module processes biomedical literature on the Internet for information supporting relationships between genes and diseases and gene functionality.Type: GrantFiled: June 17, 2002Date of Patent: October 28, 2008Assignee: Health Discovery CorporationInventors: Isabelle Guyon, Edward P. Reiss, René Doursat, Jason Aaron Edward Weston
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Publication number: 20080215513Abstract: 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 (l0-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.Type: ApplicationFiled: October 30, 2007Publication date: September 4, 2008Inventors: Jason Aaron Edward Weston, Andre' Elisseeff, Bernard Schoelkopf, Fernando Perez-Cruz
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Patent number: 7318051Abstract: 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.Type: GrantFiled: May 20, 2002Date of Patent: January 8, 2008Assignee: Health Discovery CorporationInventors: Jason Aaron Edward Weston, André Elisseeff, Bernhard Schoelkopf, Fernando Pérez-Cruz
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Patent number: 7117188Abstract: The methods, systems and devices of the present invention comprise use of Support Vector Machines and RFE (Recursive Feature Elimination) for the identification of patterns that are useful for medical diagnosis, prognosis and treatment. SVM-RFE can be used with varied data sets.Type: GrantFiled: January 24, 2002Date of Patent: October 3, 2006Assignee: Health Discovery CorporationInventors: Isabelle Guyon, Jason Aaron Edward Weston