1396 results about "Categorization" patented technology

The present invention relates to methods, systems and apparatus for capturing, integrating, organizing, navigating and querying large-scale data from high-throughput biological and chemical assay platforms. It provides a highly efficient meta-analysis infrastructure for performing research queries across a large number of studies and experiments from different biological and chemical assays, data types and organisms, as well as systems to build and add to such an infrastructure. According to various embodiments, methods, systems and interfaces for associating experimental data, features and groups of data related by structure and/or function with chemical, medical and/or biological terms in an ontology or taxonomy are provided. According to various embodiments, methods, systems and interfaces for filtering data by data source information are provided, allowing dynamic navigation through large amounts of data to find the most relevant results for a particular query.

A method and apparatus aids consistent, high-quality input of meta-information associated with items inserted into a database by coupling a hierarchical subject taxonomy, used to definitively assign an element, with sets of attributes appropriate for each category. Each attribute in turn is itself associated with a set of legal values drawn from a universe of appropriately typed values. The method and apparatus can be used to enable a user of a database management system to input or augment a set of semantically relevant and consistent meta-information associated with content in or being placed into the database management system. The content in the database system is placed into one or more of a set of hierarchical taxonomic categories. Zero or more semantically relevant attributes are associated with each taxonomic category. Relevant sets of values for each attribute, drawn from a universe of appropriate values, are associated with each attribute at each level in the taxonomic hierarchy. The placement of an element into a category adds the hierarchical set of attributes associated with that category to those relevant to the element. The value sets associated with those attributes may then be used to select consistent and appropriate meta-information to be associated with the element.

A common software interface simplifies a process of configuring the network security features provided by network controlled devices. A real-time threat entity detection system automatically scans the network using various protocols and builds entity profile data for each detection. The entity profile data is saved and updated every time the entity is detected on the network. Once the scan is complete, the system user is prompted to classify each newly detected node as a member or non-member of the network. The system user can then define automatic actions to take upon identification of the existence of the defined threat entity on the network at any point in the future. For example, a typical action could include notifying the threat entity of its detection or sending continuous requests to the threat entity over the network to effectively eliminate the usefulness of its membership on the network. The software also contacts the network gateway or router and configures MAC address filtering and disables broadcast of the router's SSID, effectively making the network invisible to any devices other than the devices allowed on the network. Additionally, the solution provides a process to add new members to the network while security features are enabled.

In categorizing an object respective to at least two categorization dimensions each defined by a plurality of categories, a probability value indicative of the object is determined for each category of each categorization dimension. A categorization label for the object is selected respective to each categorization dimension based on (i) the determined probability values of the categories of that categorization dimension and (ii) the determined probability values of categories of at least one other of the at least two categorization dimensions.

The present invention relates generally to the classification of items into categories, and more generally, to the automatic selection of different classifiers at different places within a hierarchy of categories. An exemplary hierarchical categorization method uses a hybrid of different classification technologies, with training-data based machine-learning classifiers preferably being used in those portions of the hierarchy above a dynamically defined boundary in which adequate training data is available, and with a-priori classification rules not requiring any such training-data being used below that boundary, thereby providing a novel hybrid categorization technology that is capable of leveraging the strengths of its components. In particular, it enables the use of human-authored rules in those finely divided portions towards the bottom of the hierarchy involving relatively close decisions for which it is not practical to create in advance sufficient training data to ensure accurate classification by known machine-learning algorithms, while still facilitating eventual change-over within the hierarchy to machine learning algorithms as sufficient training data becomes available to ensure acceptable performance in a particular sub-portion of the hierarchy.

In a method of detecting motion of a particular part from a sequence of successive input images, motion vectors of a plurality of parts of the input images are detected by tracking feature points in the successive input images, the motion of the particular part is detected by processing the motion vectors. The motion detection is performed by producing a histogram of the motion vectors, detecting a peak corresponding to the particular part from the histogram; classifying feature points into a class of feature points belonging to the peak detected in the histogram processing step and a class of the other feature points, and calculating motion of the particular part by analysing the motion vectors belonging to the peak detected in the histogram processing step. The histogram processing step includes detecting the peak corresponding to the particular part on the basis of the class information defined for feature points.

The present invention aims to provide a data processing device that provides a result of categorization that is satisfactory to a user, even when user data includes an object specific to the user. The data processing device stores therein model data pieces each indicating detection counts of feature amounts; judges, for each target data piece, whether the target data piece is a non-categorization data piece including an uncategorizable object, using the model data pieces and the detection count of each of at least two feature amounts detected in the target data piece; when, as a result of the judgment, two or more of the target data pieces are judged to be non-categorization data pieces, specifies at least two feature amounts that are each included, and detected the same number of times, in a predetermined number or more of the non-categorization data pieces, and newly creates a model data piece based on the at least two feature amounts that have been specified, using a class creation method, and stores the model data piece into the storage unit.

The invention belongs to the data mining and natural language processing technical field, specifically a product comment analyzing method and system with learning supervising function. According to the method, multiple categories (product features) are artificially defined aiming at a specific product; firstly, product feature aspect classification is carried out to collected user comments by a machine learning and training classifier successively; then emotion analysis is carried out to the comment texts classified by a training classifier; and finally quantitative comments on each feature of the product are summarized for a user through comprehensively counting the product features related to a great deal of comment texts and corresponding emotional tendencies. The method and the system of the invention are relatively efficient, rapid, simple and convenient; the classified contents are all user concerned contents; the analysis result is provided to the user visually and clearly; and the work of viewing a great deal of comments can be removed.

A class is to be characterized of a probabilistic classifier or clustering system that includes probabilistic model parameters. For each of a plurality of candidate words or word combinations, divergence of the class from other classes is computed based on one or more probabilistic model parameters profiling the candidate word or word combination. One or more words or word combinations are selected for characterizing the class as those candidate words or word combinations for which the class has substantial computed divergence from the other classes.

Provided is a process including: receiving a first write request from an application requesting to write data to a first remote database via a network; classifying the second value as higher-security; and in response to classifying the second value as higher-security, redirecting the second value from a destination specified by the application in the received first write request, wherein an attacker with full access to the first database does not have access to the second value classified as higher security, and wherein code of the application is not modified to interface with two databases in place of the first remote database.

Provided is an analyzer capable of analyzing an analyte with an analytical condition corresponding to a minor classification prepared by subdividing the species (major classification) of the analyte. This analyzer selects a major classification corresponding to the analyte to be analyzed from a plurality of major classifications indicating the types of analytes, preferentially displays minor classifications, belonging to the selected major classification, prepared by subdividing the corresponding major classification, selects one of the displayed minor classifications, and analyzes the analyte on the basis of an analytical condition corresponding to the selected minor classification.

Provided is a process that includes: obtaining a corpus of unstructured natural language text statements and corresponding responses by responding users, wherein the corresponding responses are responsive natural language text statements or responding-user-expressed scores; obtaining demographic features associated with the responding users; scoring the corresponding responses based on whether the corresponding responses indicate offense to the unstructured natural language text statements to which the corresponding responses correspond in order to form offensiveness scores; forming a training set at least in part by: labeling the unstructured natural language text statements, or n-grams therein, with labels based on the offensiveness scores; and associating the labels with corresponding demographic features of the responding users; and causing a machine learning model to be trained based on the training set, wherein the machine learning model is configured to at least one of: classify natural language utterances as offensive or non-offensive, or generate utterances.

The invention discloses a text entity relationship extraction method and a model training method, and can be applied to a natural language processing technology in the field of artificial intelligence; according to the invention, the graph state recurrent neural network and the BERT model are combined, a first vector used for representing semantic features of the text and a second vector used forrepresenting dependency relationship features of the text are extracted from the text; the first vector and the second vector are spliced and then classified; the relationship extraction of the entitypairs is enabled to obtain relatively high accuracy in long sentence and cross-sentence application scenes; the problem of insufficient accuracy in application scenes such as long sentences and crosssentences in the prior art is solved, in addition, in the model training stage, based on the preset rules and the pre-training model, a large amount of annotation data is produced in a remote supervision mode, and a large amount of accurate training data can be obtained at a low cost. Therefore, the method can be widely applied to the natural language processing technology.

A prediction processing system, method, and method for doing business is disclosed. The prediction processing system can collect, process and publish event-outcome information. The prediction processing system can dynamically filter participants into groupings and iteratively optimize odds calculations over time. A proposal framework consisting of a various abstract proposal types can model and settle propositions. Game propositions can be automatically generated based on event categorization relationships. The prediction processing system can provide game players or others with access to collective intelligence, including prediction information and information derived from prediction information. The prediction processing system can create groupings of better-performing predictors and provide them with additional information not generally available. Better-performing predictors can be provided with additional stakes to increase the weight of their predictions in odds calculations. The prediction processing system can provide access to event-outcome information on a for-fee subscription basis.

The invention relates to the Internet searching technology and aims to provide a method for searching, integrating and providing comment information based on semantics and a searching system. The method comprises the following steps of: discovering and integrating character comment information by utilizing a searching engine; classifying and summarizing comment texts according to a theme; particularly, the method also comprises the following steps of: extracting the non-character comment information; unearthing the semantic contact between the non-character comment information and character comment information; and integrating the two types of information on the basis so as to provide a searching service need. By the method, the estimation, the integration and the summarization of the isomeric information (namely character information and non-character information) and hierarchical comment content are realized. The searching engine is endowed with new functions of discovering, establishing and managing the comment information relevant to the comment object so that more and more thoughtful use experiences are brought and better service can be provided for extensive users.

The invention discloses a sparse representation and dictionary learning-based bearing fault classification method. The method is characterized by comprising the following steps of: preprocessing acquired history bearing vibration signals to obtain vibration signals under different working states, respectively constructing corresponding sub-dictionaries and combining the sub-dictionaries into a redundant dictionary; and online acquiring a bearing vibration signal by using a sensor, solving a sparse coefficient of the signal under the redundant dictionary by using a generalized orthogonal matching pursuit algorithm, and classifying the vibration signal through a reconstruction error so as to identify the bearing working state. According to the method, relatively good classification effect can be obtained, the dictionary training process can be accelerated and the ability, of being adapted to target signals, of the dictionary is improved, so that the method is capable of realizing the sparse decomposition of complex vibration signals more efficiently and is used for fault identification.