50 results about "Statistical classifier" patented technology

A network data classifier statistically classifies received data at wire-speed by examining, in part, the payloads of packets in which such data are disposed and without having a priori knowledge of the classification of the data. The network data classifier includes a feature extractor that extract features from the packets it receives. Such features include, for example, textual or binary patterns within the data or profiling of the network traffic. The network data classifier further includes a statistical classifier that classifies the received data into one or more pre-defined categories using the numerical values representing the features extracted by the feature extractor. The statistical classifier may generate a probability distribution function for each of a multitude of classes for the received data. The data so classified are subsequently be processed by a policy engine. Depending on the policies, different categories may be treated differently.

The present invention relates to a system and method for identifying and tracking gaming objects. The system comprises an overhead camera for capturing an image the table, a detection module for detecting a feature of the object on the image, a search module for extracting a region of interest of the image that describes the object from the feature, a search module for extracting a region of interest of the image that describes the object from the feature, a feature space module for transforming a feature space of the region of interest to obtain a transformed region of interest, and an identity module comprising a statistical classifier trained to recognize the object from the transformed region. The search module is able to extract a region of interest of an image from any detected feature indicative of its position. The system may be operated in conjunction with a card reader to provide two different sets of playing card data to a tracking module, which may reconcile the provided data in order to detect inconsistencies with respect to playing cards dealt on the table.

The present invention provides systems, methods, computer program products, and combinations and subcombinations thereof for filtering received messages. The invention includes a client system having filtering logic, a statistical classifier module, and a classifier database. The classifier module is automatically trained, without manual intervention, when the score generated by the classifier for a received message is inconsistent with a user-defined authoritative status indication. In addition, the classifier module is initialized using user-identified messages in a ratio representative of the ratio of non-spam to spam messages received by the user.

The present invention includes a system and method for automatically extracting the demographic information from images. The system detects the face in an image, locates different components, extracts component features, and then classifies the components to identify the age, gender, or ethnicity of the person(s) in the image. Using components for demographic classification gives better results as compared to currently known techniques. Moreover, the described system and technique can be used to extract demographic information in more robust manner than currently known methods, in environments where high degree of variability in size, shape, color, texture, pose, and occlusion exists. This invention also performs classifier fusion using Data Level fusion and Multi-level classification for fusing results of various component demographic classifiers. Besides use as an automated data collection system wherein given the necessary facial information as the data, the demographic category of the person is determined automatically, the system could also be used for targeting of the advertisements, surveillance, human computer interaction, security enhancements, immersive computer games and improving user interfaces based on demographic information.

An automated voice message or caller prioritization system that extracts words, prosody, and/or metadata from a voice input. The data extracted is classified with a statistical classifier into groups of interest. These groups could indicate the likelihood that a call is urgent versus nonurgent, from someone the user knows well versus someone that the user only knows casually or not at all, from someone using a mobile phone versus a landline, or a business call versus a personal calls. The system then can determine an action based on results of the groups, including the display of likely category labels on the message. Call handling and display actions can be defined by user preferences.

A system and method facilitating lithography defect solution generation is provided. The invention includes a defect solution component and a defect alert component. The defect solution component provides potential solution(s) to a defect within the lithography process utilizing artificial intelligence technique(s) (e.g., Bayesian learning methods that perform analysis over alternative dependent structures and apply a score, Bayesian classifiers and other statistical classifiers, including decision tree learning methods, support vector machines, linear and non-linear regression and/or neural network).

This invention relates to one print Mongolia character identification method, which comprises the following steps: extracting the character integral types information and character information pre-sorting to determine the input characters type; then drawing the direction property of the strokes information; on this base adopting two steps for property optimization process; finally by use of MQDF to realize sort judgment of statistic sorter.

A method of processing a mammogram image to derive a value for a parameter useful in detecting differences in breast tissue in subsequent images of the same breast or relative to a control group of such images, said derived parameter being an aggregate probability score reflecting the probability of the image being a member of a predefined class of mammogram images, comprises computing for each of a multitude of pixels within a large region of interest within the image a pixel probability score assigned by a trained statistical classifier according to the probability of said pixel belonging to an image belonging to said class, said pixel probability being calculated on the basis of a selected plurality of features of said pixels, and computing said parameter by aggregating the pixel probability scores over said region of interest. Saud features may include the 3-jet of said pixels.

A method for detecting change in business data using a statistical classifier process. The method includes inputting a first set of business data in a first format from a real business process from a first data source and storing the first set of business data into one or more memories. The method also includes inputting a second set of business data in a second format from a real business process from a second data source and storing the second set of business data into one or more memories. The method forms a statistical classifier by inputting the first set of business data into a learning process associating with the statistical classifier that processes business the data in the first format. The method stores the classifier into the one or more memories, the classifier being associated with the first set of data in the first format and processes the data from the first data source in the statistical classifier to derive a first result. The method also processes the data from the second data source in the statistical classifier to derive a second result and determines a behavior of the statistical classifier based upon at least the first result and the second result. The method displays information associated with the behavior of the statistical classifier.

The present invention involves using one or more statistical classifiers in order to perform task classification on natural language inputs. In another embodiment, the statistical classifiers can be used in conjunction with a rule-based classifier to perform task classification.

Systems, methods, and apparatus are described that provide for analysis of seismic data. Features of temporal gait patterns can be extracted from seismic/vibration data. A mean temporal gait pattern can be determined. A statistical classifier can be used to model features of the data. The model can be used to classify the data. As a result, discrimination of seismic sources can be performed. Systems for discrimination of seismic data are also described. A system can include a vibration sensor system configured and arranged to detect vibrations. A system can also include a processor system configured and arranged to receive data from the vibration sensor, recognize the seismic data as belonging to a particular class of seismic data, and produce an output signal corresponding to the recognized particular class of seismic data.

The present invention discloses an informational computation method for classifying objects Specifically, the invention is a system, method, and computer-readable media for classifying tumors using a nonparametric statistical classifier in conjunction with an artificial neural network. The invention classifies unknown tumor types based on the correlation of unknown tumor's genetic expression compared to the genetic expression of know tumor types by first performing a nonparametric statistical analysis on the know data, training a artificial neural network with the known data, and then inputting the unknown tumor data into the neural network to calculate the probability that the sample tumor is a member of a class of tumors. By using a statistical classifier in conjunction with a neural network, the invention classifies unknown tumors more accurately then conventionally possible. Advantageously, by using a variety of tumor genetic expression data sets, including both published data sets and generated data sets, a tumor classifier, robust and accurate enough for clinical application, is provided.

The invention provides a printed font character identification method based on Arabic character set which comprises, extracting region information, character font information, and constituent part information unique for Arabic character set, carrying presorting, determining character class subset of the input character, extracting direction characteristic for reflecting the character stroke composition information, finally employing two steps of characteristic optimization processing including, characteristic dressing, eigen transformation by integrating linear discrimination analysis (LDA) and K-L transformation, finally proceeding categorization judgment through modified quadratic discriminating function (MQDF) statistical classification device.

Embodiments of the present invention disclose a live content screening method, apparatus and device based on the bullet screen, and a storage medium. The method comprises: determining a scene categoryto which the live content belongs, and selecting a word segmentation thesaurus based on the scene category; obtaining the bullet screen of the live content, and selecting a rating word from the bullet screen by using the word segmentation thesaurus; collecting statistics of the word frequency of the rating word and generating a rating word feature vector according to the rating word and the corresponding word frequency; and inputting the rating word feature vector into a statistical classifier or a neural network classifier, and outputting a screening result according to the statistical classifier or the neural network classifier. According to the technical scheme of the embodiments of the present invention, a high-precision screening result can be automatically obtained by using the statistical classifier or neural network classifier, the evaluation criteria of the live content can be unified, and the evaluation speed is fast and the efficiency is more efficiently.

The present invention provides methods, systems, and code for accurately classifying or diagnosing a sample from an individual as an IBS sample. The methods and systems of the present invention are useful for ruling out one or more diseases or disorders that share a similar clinical presentation as IBS followed by identifying (i.e., ruling in) IBS using statistical algorithm(s) and/or empirical data. In particular, the methods and systems of the present invention use a first combination of learning statistical classifier systems to rule out IBD with an accuracy of greater than about 90% and a second combination of learning statistical classifier systems to rule in IBS in a non-IBD sample with an accuracy of greater than about 80%.

Some aspects include a method of detecting change in degree of midline shift in a brain of a patient. While the patient remains positioned within the low-field magnetic resonance imaging device, acquiring first magnetic resonance (MR) image data and second MR image data of the patient's brain; providing the first and second MR data as input to a trained statistical classifier to obtain corresponding first and second output, identifying, from the first output, at least one initial location of at least one landmark associated with at least one midline structure of the patient's brain; identifying, from the second output, at least one updated location of the at least one landmark; and determining a degree of change in the midline shift using the at least one initial location of the at least one landmark and the at least one updated location of the at least one landmark.