235 results about "Statistical classification" patented technology

The present invention is directed to methods of and systems for ranking results returned by a search engine. A method in accordance with the invention comprises determining a formula having variables and parameters, wherein the formula is for computing a relevance score for a document and a search query; and ranking the document based on the relevance score. Preferably, determining the formula comprises tuning the parameters based on user input. Preferably, the parameters are determined using a machine learning technique, such as one that includes a form of statistical classification.

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.

A method of determining relevancies of objects to a search query includes associating multiple tags with multiple objects, recording bookmarks to the multiple objects, or both, and determining a relevance score for each of the multiple objects and a search query. One embodiment of the method combines full-text relevance algorithms with tag relevance algorithms. Other embodiments include statistical relevance algorithms such as statistical classification or rank regression algorithms. When a user executes a search query, a results list containing the objects is returned, with the objects organized based on the relevance scores. The objects are organized by, for example, listing those with the highest relevance scores first or by marking them with an indication of their relevance. Preferably, relevance scores for a tag-object pair are based on a number of times a term in the tag has been associated with the object, a number of tags associated with the object, a number of times that the tag has been associated with the multiple objects, a number of tag-object pairs that contain a term in the tag, a number of tag-object pairs that contain a reference to the object, or any combination of these.

The disclosure relates to methods, systems and other embodiments directed to determining an information domain match for a natural language (NL) input (e.g., a spoken utterance), and confirming whether the NL input is correctly matched to the information domain. For example, after receiving an NL input, a first information domain to which the NL input belongs and a feature value set may be determined based on a semantic pattern matching technique. Further, a second information domain to which the NL input belongs, and a corresponding confidence score related to the second information domain may be determined. The second information domain may be determined based on a first statistical classification technique. Based on the determined feature value set and the confidence score related to the second information domain, it may be confirmed whether the NL input correctly belongs to the first information domain, e.g., based on a second statistical classification technique.

A method, information processing system, and computer program storage product for managing information within an electronic file are provided. A plurality of information sets within an electronic file is analyzed. At least one of the information sets is compared to at least one statistical classification model. The statistical classification model includes one or more probabilities associated with a plurality of analyzed information sets that indicate a likelihood that a respective analyzed information set is classified sensitive information. The at least one information set is determined to substantially match at least one analyzed information set in the statistical classification model. The probability associated with the at least one analyzed information set is determined whether to be above a threshold. The at least one information set is classified as sensitive information in response to determining that the probability is above the threshold.

Surgical monitoring techniques detect surgical contexts, such as induction, intubation, preparation and positioning, incision, maintenance, emergence, recovery, and / or therapy administration based on information received from a patient monitor and / or other medical equipment, which can be compared to predetermined data patterns, which can be based on heuristic or statistical classifications. With detectors and / or controllers operating in conjunction therewith and in various combinations thereof, responses can also be invoked and / or also overridden.

A Markov Random Field (MRF)-based technique is described for performing clustering of images characterized by poor or limited data. The proposed method is a statistical classification model that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the segmentation output with respect to the result of pure K-means clustering.

The invention provides methods for treating patients which methods comprise methods for predicting responses of cells, such as tumor cells, to treatment with therapeutic agents. These methods involve measuring, in a sample of the cells, levels of one or more components of a cellular network and then computing a Network Activation State (NAS) or a Network Inhibition State (NIS) for the cells using a computational model of the cellular network. The response of the cells to treatment is then predicted based on the NAS or NIS value that has been computed. The invention also comprises predictive methods for cellular responsiveness in which computation of a NAS or NIS value for the cells (e.g., tumor cells) is combined with use of a statistical classification algorithm. Biomarkers for predicting responsiveness to treatment with a therapeutic agent that targets a component within the ErbB signaling pathway are also provided.

The present invention is directed to methods of and systems for ranking results returned by a search engine. A method in accordance with the invention comprises determining a formula having variables and parameters, wherein the formula is for computing a relevance score for a document and a search query, and ranking the document based on the relevance score. Preferably, determining the formula comprises tuning the parameters based on user input. Preferably, the parameters are determined using a machine learning technique, such as one that includes a form of statistical classification.

The invention provides a musical / unmusical real-time detection method and a device thereof; the method comprises the following steps: a signal is preprocessed; the short-time feature of the preprocessed signal is calculated; after reaching an analysis step length, the root mean square of cumulative short-time energy is calculated to judge whether the signal is in a silent state; if the signal is determined to be in a non-silent state, long-time feature parameters are calculated according to short-time analysis parameters; according to the calculated long-time feature parameters, the signal is counted and sorted; according to a Gaussian mixture model, whether the sort of the sound in a current analysis interval is a music / non-music is determined; the signal is post-processed to remove a mutation state. The musical / nonmusical real-time detection method realizes musical / nonmusical steady and effective real-time detection or segmentation and is combined with voice activity detection to form an integrated voice activity detection proposal.

The invention provides methods for determining the probability that a given region of a tissue sample contains tissue of a given category, such as CIN 1 (cervical intraepithelial neoplasia, grade 1), CIN 2 / 3 (cervical intraepithelial neoplasia grades 2 and / or 3), normal squamous, normal columnar, and metaplasia, for example. The invention provides increased diagnostic accuracy by combining a plurality of statistical classification techniques. Furthermore, in one embodiment, the invention comprises combining one or more statistical techniques with one or more non-statistical classification techniques.

The invention discloses an information recommending method and system. The information recommending method includes steps of obtaining network source recommendation information which is relevant to programs being played, classifying the obtained network source recommendation information according to element feature categories of the programs, counting the number of the categories of the classified programs as well as information release time, calculating element feature weights of the programs being played by summarizing the information number and the information release time, calculating a similarity between the recommendation information of network sources and the programs being played according to element features and the element feature weights of the programs being played as well as information features and information feature weights of the recommendation information, sequencing the network source recommendation information according to a sequencing strategy based on the similarity, and recommending and extracting the categories of the recommendation information to users according to the number of the categories of the recommendation information and the ratio of the categories to the network sources so that the users can directly and methodically read relevant information.

The invention discloses a smart broadcast advertising methods, composites with networks, content distribution system, programs, advertisement and network terminals and network users. Based on network users registered users of information reflect the age, sex, occupation, region, such as the basic personal preferences and through statistical classification of network users to watch different programs and advertising time to reflect user categories, thereby Content distribution system choosing the most consistent with the needs of advertisers network type ads user groups to enhance the effectiveness of advertising viewing. Because according to user behavior and habits of different needs, different types of advertising with the corresponding types of multimedia programming is sent to specific attributes of the crowd realized the minimum cost of advertising and advertising effectiveness and relevance of the effect of the greatest value.

A statistical system and method for generating patterns and performing online handwriting recognition based on those patterns. A plurality of predetermined patterns may be generated by performing feature extraction operations on one or more character samples utilizing a Gabor filter. An online handwritten character may be acquired. The online handwritten character may be pre-processed. One or more feature extraction operations, utilizing a Gabor filter, may be performed on the online handwritten character to produce a feature vector. One or more patterns may be generated, using a statistical algorithm, for the online handwritten character, based on the feature vector. The online handwritten character may be statistically classified based on a comparison between the one or more patterns generated for the online handwritten character and the plurality of predetermined patterns.

The invention discloses a linked data oriented entity classification method and system which are aimed at the problem of entity classification of linked data. The entity classification method includes pretreatment, statistical classification and post treatment. Pretreatment: word segmentation is performed on text description information in an entity page; and an attribute name of an information box and word information obtained by segmentation form an entity page character. Statistical classification: a statistical classification model is trained through various segmentation granularities to classify the entity page, and then a primary prediction result of entity class can be obtained. Post treatment: the entity statistical classification result is corrected; and a combined entity class is corrected through model combination, language knowledge, linkage information and class associate attribute information. The method and the system is easy to implement and debug, is high in efficiency, is good in accuracy, can be used for performing knowledge management on the linked data, and achieve high-precision classification of the entity.

The present invention discloses a medical big data based disease automatic assistance diagnosis system and method. The system comprises: a background data storage unit; an information processing unit, which specifically comprises: a statistical classification module, used for acquiring case data in the background data storage unit, and performing statistical classification on the case data, so as to obtain a symptom set and a definitively diagnosed disease type set; a diagnosed disease set calculation module, used for calculating a definitively diagnosed symptom set of various diseases according to the symptom set and the disease type set that are obtained by the statistical classification module; and a disease automatic diagnosis module, used for acquiring disease symptom data provided by a user, generating a selection symptom set, comparing the selection symptom set with the definitively diagnosed symptom set of various diseases and performing calculation, so as to obtain a disease determination result; and a man-machine interaction unit, used for displaying an interface of selecting a disease by the user, and outputting a disease diagnosis result. The method disclosed by the present invention is simple, easy and strong in operatability, and provides a new clinic assistant diagnosis tool for the medical field, and reduces an error / miss diagnosis rate.

The invention discloses an intelligent electricity utilization anomaly detection method for non-technical loss, and belongs to the technical field of power load analysis. The method includes the steps that (1) original data are preprocessed; (2) feature extraction is conducted on sample data; (3) samples are divided into the initial training samples and the optimization samples; (4) real-time data are sampled, and the sample features are extracted to form a test sample; (5) parameter optimization is conducted through a GA to determine the optical ELM parameter value; (6) anomaly detection is conducted by substitution of the optical ELM parameter value, a training sample and a test sample; (7) if the test time is an integer multiple of 72 hours, classification accuracy and the anomaly error detection rate are counted; if the anomaly error detection rate exceeds the set limit value, the step (8) is executed, and if not, the step (4) is executed; (8) the training sample of a user is updated and the step (5) is executed. The intelligent electricity utilization anomaly detection method for non-technical loss is definite in physical conception, clear in thought, easy and convenient to analyze and calculate, and capable of effectively solving the problem of online detection of non-technical loss of arbitrary electricity utilization loads.

A method of creating a statistical classification model for a classifier within a natural language understanding system can include processing training data using an existing statistical classification model. Sentences of the training data correctly classified into a selected class of the statistical classification model can be selected. The selected sentences of the training data can be assigned to a fringe group or a core group according to confidence score. The training data can be updated by associating the fringe group with a fringe subclass of the selected class and the core group with a core subclass of the selected class. A new statistical classification model can be built from the updated training data. The new statistical classification model can be output.

A modulation feature measurement and classification system for classifying a pulsed signal that includes a preprocessor, phase measurement system and modulation classifier. Preprocessor detects pulse starts and stops, measures pulse duration and converts the pulsed signal into digitized baseband in-phase / quadrature samples. Phase measurement system measures short chip counts, long chip counts, phase jump magnitudes, number of phase states, and polynomial coefficients for phase modulation of the pulsed signal. Modulation classifier determines modulation type based on the measurements using both rules-based and similarity-based classification methods.

The present invention is a method for extracting electromagnetic parameters of artificial electromagnetic material based on support vector machine (SVM). The invention relates to a new method for researching electromagnetic parameter measurement, capable of testing shield-hand material and artificial electromagnetic material having a periodic structure, and the testing result precision is high andthe production of testing samples is simple. The support vector machine (SVM) method is based on a VC-dimension theory of a statistical learning theory and a structure risk minimum principle, seeks an optimum compromise between the complexity and the learning capacity of a model based on limited sample information so as to obtain best popularization capability, and is widely applied to statistical classification and regression analysis. According to the invention, transmission and reflection coefficients of material to be tested are calculated by numerical computation methods FEM and FDTD ofelectromagnetism, and the corresponding computed result is used as training sequences to train the the support vector machine. When the support vector machine is trained fully, it is capable of calculating equivalent dielectric constant and equivalent magnetic conductance of the material to be tested by inputting testing values of the transmission and reflection coefficients.

The invention relates to an automatic recognition method for a woven fabric structure based on gradient direction characteristics and Fuzzy C-Means Algorithm (FCM). The automatic recognition method comprises the following steps of: firstly preprocessing a woven fabric brightness image by adopting an image morphology method, then correcting deflection existing in interweaving of warp yarns and weft yarns by utilizing gray projection, simultaneously segmenting a fabric image into a plurality of weave points, extracting gradient direction histogram characteristics on each weave point, classifying the weave points by using an improved FCM, and finally carrying out statistics on classifying results according to the periodicity of the fabric structure and correcting error checking points, thus outputting a correct weave chart. The automatic recognition method can overcome the influence brought by uneven illumination, and difference in thickness and color of yarns by utilizing gradient direction information of the weave points and combining with the FCM method, can achieve recognition of basic weaves (a plain weave, a twill weave and a satin weave) of the woven fabric, and also has a good recognition effect on derivative weaves (a plain derivative weave, a twill derivative weave and a satin derivative weave) in small decorative pattern derivative weaves.

A system and method for speech quality detection is included. The method may include receiving, at a computing device, a first speech signal associated with a particular user. The method may include extracting one or more short-term features from the first speech signal wherein extracting short-term features includes extracting a time frame of between 10-50 ms. The method may also include determining one or more statistics of each of the one or more short-term features from the first speech signal. The method may further include classifying the one or more statistics as belonging to one of a set of quality classes.

The invention provides a student comprehensive quality evaluation system which comprises the components of an acquisition module which is used for respectively acquiring student evaluation information of a school and / or student self evaluation information for each to-be-evaluated quality of the student; a scoring module which is used for determining the score of each to-be-evaluated quality according to the student evaluation information of the school and / or the student self evaluation information; and a statistics module which is used for determining a comprehensive quality report of the student according to all to-be-evaluated qualities of the student. The student comprehensive quality evaluation system determines the score of each to-be-evaluated quality of the student according to the student evaluation information of the school or the student self evaluation information, thereby determining the comprehensive quality report of the student according to all the to-be-evaluated qualities, wherein the comprehensive quality report can be effectively used by colleges in student enrollment. The student comprehensive quality evaluation system performs detailed statistics classification on the daily behavior of the student, thereby facilitating understanding by evaluation participants and users.

This invention relates to image process technique, and especially to an automatic division of brain blood shortage area based on multiple-size statistic sorting and local container sorting method, which comprises the following steps: to valuate the DTI image spreading value and direction isomerism; second to computer the size space; third to sort the multiple size; fourth to sort the local container. This invention is of high application value in medical assistant dialogue system, medical image three-dimensional recreation system and clinic disease qualitatively diagnose analysis.

The invention relates to a data classification based data leakage prevention method and system, which can further improve the data leakage prevention effect. The data classification based data leakage prevention method comprises the steps as follows: A, designing a basic data classifier, B, defining various elements of DSL (domain specific languages) to form a data leakage strategy library, C, detecting input data via the DSL strategy library, executing defined response in the strategy, and writing time which violates the strategy into a data leakage event database, D, obtaining data, E, executing defined corresponding operation on the violated data, and F, performing statistical classification on the violated data leakage events, and displaying at a client. The data classification based data leakage prevention system comprises a basic classifier management module, a DSL strategy defining and editing module, a DSL strategy analysis execution module, a data access module, a data protection response module and an event management module.

A method of classifying text input for use with a natural language understanding system can include determining classification information including a primary classification and one or more secondary classifications for a received text input using a statistical classification model (statistical model). A statistical classification sub-model (statistical sub-model) can be selectively built according to a model generation criterion applied to the classification information. The method further can include selecting the primary classification or the secondary classification for the text input as a final classification according to the statistical sub-model and outputting the final classification for the text input.

The invention relates to an underwater acoustic target recognition method based on a deep convolution generative adversarial network, and belongs to the field of underwater acoustic target recognition. The method comprises the following steps: constructing a generation model and a discrimination model; normalizing the original underwater acoustic signals with the category information and the original underwater acoustic signals without the category information, and framing; setting generation model parameters; setting hyper-parameters; constructing a convolutional neural network in the discrimination model; taking the signal data as the input of a convolutional neural network, and calculating and outputting the signal data through the network to obtain a classification result; and countingclassification errors, returning the errors by using a BP algorithm, and updating the weight parameters of the network in the three steps, including the weight between the convolution kernel and thefull connection layer, until the iteration frequency is reached. The method has the advantages that the extracted features completely depend on the data, parameters related to the data do not need tobe set manually, the extracted features are effective to a certain extent for the data, and the data can be effectively utilized to mine distribution information existing in the data.

The invention provides a method for extracting a phytocoenosium spatial structure. The method comprises: performing multi-resolution segmentation of a to-be-tested remote-sensing image in a target area to obtain remote-sensing image objects with different resolutions; establishing a corresponding relation between an image resolution of the to-be-tested remote-sensing image and an ecological organization resolution to obtain an image resolution of each plant type in the to-be-tested remote-sensing image, wherein the plant types include a meadow, a shrub, an arbor, a population and a group, wherein the meadow, the shrub, and the arbor are plant individuals; performing vegetation classification of a pre-selected sample of the to-be-tested remote-sensing image in plant individual and population image resolution according to the plant individuals and the population image resolution; summing the classification result of each resolution to a grouped data layer; and calculating plant individuals and parameters of a population spatial structure in a group resolution object boundary. The method for extracting the phytocoenosium spatial structure is relatively accurate, and is low in monitoring cost and high in objectivity.

The invention discloses a mass data GPU (graphics processing unit) wave equation reverse time migration imaging method. The overall steps of the reverse time migration involves three modules, including a data distribution module, a reverse time migration module and a real-time data merging module, with the data distribution module, single shot data are assigned to the different GPU nodes; with the reverse time migration module, the single shot data are calculated for imaging to output an imaging file; with the real-time data merging module, the imaging file is scanned in real time and is subjected to statistic classification, the number and size of the information files required by a user are calculated, and the user is notified about how much time the user needs to complete the data merging in the form of percentage. In the mass data GPU inverse wave equation migration imaging method of the invention, the three modules can run independently in parallel, and are more suitable for massdata reverse time migration implementation, so that the computing efficiency is high, the computing speed is fast, fault-tolerant capability is high, and the problem of insufficient GPU memory for the mass imaging can be effectively overcome.