Abstract: Embodiments of the invention provide a system including a first logic module for receiving a data stream that includes at least one neural network configured to generate at least one first password sample based at least in part on at least a portion of the data stream. A second logic module can be operatively coupled to the first logic module to receive the first password sample and at least one input dataset including a second password sample. The system can perform calculations to distinguish between at least one password of the first password sample and at least one password of the second password sample. Further, the system can iteratively learn and produce a feedback dataset based on the calculations, where the feedback dataset is configured to be provided to the first logic module.

Abstract: Embodiments of the invention provide a system including a first logic module for receiving a data stream that includes at least one neural network configured to generate at least one first password sample based at least in part on at least a portion of the data stream. A second logic module can be operatively coupled to the first logic module to receive the first password sample and at least one input dataset including a second password sample. The system can perform calculations to distinguish between at least one password of the first password sample and at least one password of the second password sample. Further, the system can iteratively learn and produce a feedback dataset based on the calculations, where the feedback dataset is configured to be provided to the first logic module.

Abstract: The method includes at least one processor estimating a first set of bias errors of a first set of base stations by obtaining a first set of time-of-arrival (ToA) measurements associated with the first set of base stations and a first set of devices, the first set of devices having known physical locations. The processor determines input parameters using the first set of bias errors, the input parameters including a first set of antenna locations for the first set of base stations, and a second set of ToA measurements associated with the first set of base stations and a first object, and determines a first object location for the first object using the input parameters.

Abstract: Various embodiments provide a method and apparatus for providing improved anchor-anchor clock synchronization. In particular, the skew and offset of a second clock in reference to a first clock is determined based on measured transmit and receive times of at least two two-way transmissions between a first anchor using the first clock and a second anchor using the second clock.

Abstract: Various embodiments provide a method and apparatus for providing improved anchor-anchor clock synchronization. In particular, the skew and offset of a second clock in reference to a first clock is determined based on measured transmit and receive times of at least two two-way transmissions between a first anchor using the first clock and a second anchor using the second clock.

Abstract: Various embodiments provide a method and apparatus for determining non-line of sight (NLOS) bias estimation based on estimated NLOS bias distributions at a number of anchors and the time of arrival (ToA) of a number of tag messages received at the anchors. In particular, using anchor redundancy, tag locations corresponding to the tag messages are estimated based on the NLOS bias distributions and the ToA and then the tag locations are used to update the NLOS bias distributions. The process of determining tag locations and updating the NLOS bias distributions is repeated until the NLOS bias distributions converge.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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, transductive feature selection and single feature using margin-based ranking. 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.

Abstract: 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.

Abstract: A computer-implemented method is provided for ranking features within a large dataset containing a large number of features according to each feature's ability to separate data into classes. For each feature, a support vector machine separates the dataset into two classes and determines the margins between extremal points in the two classes. The margins for all of the features are compared and the features are ranked based upon the size of the margin, with the highest ranked features corresponding to the largest margins. A subset of features for classifying the dataset is selected from a group of the highest ranked features. In one embodiment, the method is used to identify the best genes for disease prediction and diagnosis using gene expression data from micro-arrays.

Abstract: 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, transductive feature selection and single feature using margin-based ranking. 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Features are preprocessed (204) to minimize classification error in a Support Vector Machines (200) used to identify patterns in large databases. Pre-processing (204) is performed to constrain features used to train (210) the SVM learning machine. Live data (226) is collected and processed (232) with SVM.