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3918 results about "Feature (machine learning)" patented technology
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In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.
Machine learning based botnet detection with dynamic adaptation
Embodiments of the invention address the problem of detecting bots in network traffic based on a classification model learned during a training phase using machine learning algorithms based on features extracted from network data associated with either known malicious or known non-malicious client and applying the learned classification model to features extracted in real-time from current network data. The features represent communication activities between the known malicious or known non-malicious client and a number of servers in the network.
Owner:THE BOEING CO
Intelligent autofill
InactiveUS20050257148A1Minimize effortData augmentationData processing applicationsMetal-working feeding devicesDatabaseCentral repository
The present invention provides a unique system and method that can employ machine learning techniques to automatically fill one or more fields across a diverse array of web forms. In particular, one or more instrumented tools can collect input or entries of form fields. Machine learning can be used to learn what data corresponds to which fields or types of fields. The input can be sent to a central repository where other databases can be aggregated as well. This input can be provided to a machine learning system to learn how to predict the desired outputs. Alternatively or in addition, learning can be performed in part by observing entries and then adapting the autofill component accordingly. Furthermore, a number of features of database fields as well as constraints can be employed to facilitate assignments of database entries to form values—particularly when the web form has never been seen before by the autofill system.
Owner:MICROSOFT TECH LICENSING LLC
Resolving and merging duplicate records using machine learning
InactiveUS20160357790A1Digital data information retrievalProbabilistic networksFeature vectorMachine learning
According to various embodiments of the present invention, an automated technique is implemented for resolving and merging fields accurately and reliably, given a set of duplicated records that represents a same entity. In at least one embodiment, a system is implemented that uses a machine learning (ML) method, to train a model from training data, and to learn from users how to efficiently resolve and merge fields. In at least one embodiment, the method of the present invention builds feature vectors as input for its ML method. In at least one embodiment, the system and method of the present invention apply Hierarchical Based Sequencing (HBS) and / or Multiple Output Relaxation (MOR) models in resolving and merging fields. Training data for the ML method can come from any suitable source or combination of sources.
Owner:XANT INC
Real time context learning by software agents
InactiveUS7296007B1Cathode-ray tube indicatorsKnowledge representationTheoretical computer scienceEngineering
Owner:YEN WEI
Using sequencing and timing information of behavior events in machine learning to detect malware
ActiveUS8401982B1Memory loss protectionError detection/correctionFeature vectorTheoretical computer science
A decision tree for classifying computer files is constructed. A set of training files known to be legitimate or malicious are executed and their runtime behaviors are monitored. When a behavior event is detected for one of the training file at a point in time, a feature vector is generated for that training file. Behavior sequencing and timing information for the training file at that point in time is identified and encoded in the feature vector. Feature vectors for each of the training files at various points in time are fed into a decision tree induction algorithm to construct a decision tree that takes into account of the sequencing and timing information.
Owner:NORTONLIFELOCK INC
Machine learning methods and systems for identifying patterns in data
ActiveUS20100063948A1Reduce dimensionalityPromote resultsDigital data processing detailsKernel methodsData classSupport vector machine
Methods for training machines to categorize data, and / or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples. Also provided are tools, systems, and devices, such as support vector machines (SVMs) and other methods and features, software implementing the methods and features, and computers or other processing devices incorporating and / or running the software, where the methods and features, software, and processors utilize specialized methods to analyze data.
Owner:DIGITAL INFUZION
Machine learning based anomaly detection
ActiveUS20170353477A1Mathematical modelsComputer security arrangementsUnsupervised learningSupervised learning
The technology disclosed relates to machine learning based anomaly detection. In particular, it relates to constructing activity models on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, it relates to detecting anomalies in near real-time streams of security-related events of one or more tenants by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant. It further includes determining an anomaly score for a production event based on calculated likelihood coefficients of categorized feature-value pairs and a prevalencist probability value of the production event comprising the coded features-value pairs.
Owner:NETSKOPE INC
Methods and systems of spatiotemporal pattern recognition for video content development
ActiveUS20170238055A1Facilitate decision-makingIncrease entertainmentImage enhancementTelevision system detailsEvent typeRecognition algorithm
Providing enhanced video content includes processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of a plurality of events and to determine at least one event type for each of the plurality of events. The event type includes an entry in a relationship library detailing a relationship between two visible features. Extracting and indexing a plurality of video cuts from the video feed is performed based on the at least one event type determined by the understanding that corresponds to an event in the plurality of events detectable in the video cuts. Lastly, automatically and under computer control, an enhanced video content data structure is generated using the extracted plurality of video cuts based on the indexing of the extracted plurality of video cuts.
Owner:GENIUS SPORTS SS LLC
Extracting data from semi-structured information utilizing a discriminative context free grammar
InactiveUS20060245641A1Easy to extract dataImprovement in error reductionCharacter and pattern recognitionNatural language data processingDiscriminantContext independent
A discriminative grammar framework utilizing a machine learning algorithm is employed to facilitate in learning scoring functions for parsing of unstructured information. The framework includes a discriminative context free grammar that is trained based on features of an example input. The flexibility of the framework allows information features and / or features output by arbitrary processes to be utilized as the example input as well. Myopic inside scoring is circumvented in the parsing process because contextual information is utilized to facilitate scoring function training.
Owner:MICROSOFT TECH LICENSING LLC
Android malicious application detection method and system based on multi-feature fusion
ActiveCN107180192AImprove detection accuracyReflect malicious appsCharacter and pattern recognitionPlatform integrity maintainanceFeature vectorAnalytical problem
The invention discloses an Android malicious application detection method and system based on multi-feature fusion. The method comprises the following steps that: carrying out decompilation on an Android application sample to obtain a decompilation file; extracting static features from the decompilation file; operating the Android application sample in an Android simulator to extract dynamic features; carrying out feature mapping on the static features and the dynamic features by the text Hash mapping part of a locality sensitive Hash algorithm, mapping to a low-dimensional feature space to obtain a fused feature vector; and on the basis of the fused feature vector, utilizing a machine learning classification algorithm to train to obtain a classifier, and utilizing the classifier to carry out classification detection. By use of the method, the high-dimensional feature analysis problem of a malicious code rare sample family can be solved, and detection accuracy is improved.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY
Advanced URL and IP features
ActiveUS20050022031A1Well formedDigital data processing detailsUser identity/authority verificationIp addressSpamming
Disclosed are systems and methods that facilitate spam detection and prevention at least in part by building or training filters using advanced IP address and / or URL features in connection with machine learning techniques. A variety of advanced IP address related features can be generated from performing a reverse IP lookup. Similarly, many different advanced URL based features can be created from analyzing at least a portion of any one URL detected in a message.
Owner:MICROSOFT TECH LICENSING LLC
Case management system using a medical event forecasting engine
InactiveUS20160328526A1Reduce biasEasy to discover episodic progressionMedical data miningEnsemble learningDynamic featureEngineering
A case management tool uses a novel event forecast engine. The engine leverages a flexible and extensible data structure that combines diverse formats of claims (e.g., both medical and pharmacy) and that highlights “episodes” rather than items. The engine also makes predictions with respect to “cohorts” groups, where cohorts are defined using both static and dynamic features, the latter being features that change their values based on observation periods. Multiple definitions of cohorts are implemented, and optimal cohort definitions are estimated. The engine uses rolling time window processing to extract dynamic features in the data sets, and then one or more machine learning algorithms are applied to the extracted data. Cohort-wise machine learning models preferably learn on dynamic features, which are then put together for final predictions. A validation step is applied when outcomes are later observed. Validation results update the cohort definitions as well as the model parameters.
Owner:ACCORDION HEALTH INC
Real time context learning by software agents
InactiveUS20070260567A1Function increaseKnowledge representationMachine learningTheoretical computer scienceEngineering
Providing dynamic learning for software agents in a simulation. Software agents with learners are capable of learning from examples. When a non-player character queries the learner, it can provide a next action similar to the player character. The game designer provides program code, from which compile-time steps determine a set of raw features. The code might identify a function (like computing distances). At compile-time steps, determining these raw features in response to a scripting language, so the designer can specify which code should be referenced. A set of derived features, responsive to the raw features, might be relatively simple, more complex, or determined in response to a learner. The set of such raw and derived features form a context for a learner. Learners might be responsive to (more basic) learners, to results of state machines, to calculated derived features, or to raw features. The learner includes a machine learning technique.
Owner:YEN WEI
License plate optical character recognition method and system
ActiveUS20130182910A1Efficient managementImprove performanceCharacter recognitionImage segmentationOptical character recognition
A method and system for recognizing a license plate character utilizing a machine learning classifier. A license plate image with respect to a vehicle can be captured by an image capturing unit and the license plate image can be segmented into license plate character images. The character image can be preprocessed to remove a local background variation in the image and to define a local feature utilizing a quantization transformation. A classification margin for each character image can be identified utilizing a set of machine learning classifiers each binary in nature, for the character image. Each binary classifier can be trained utilizing a character sample as a positive class and all other characters as well as non-character images as a negative class. The character type associated with the classifier with a largest classification margin can be determined and the OCR result can be declared.
Owner:CONDUENT BUSINESS SERVICES LLC
Systems and methods for predicting outcomes using a prediction learning model
PendingUS20160267397A1Multimedia data clustering/classificationProbabilistic networksTraining data setsData point
A method comprises receiving a network of a plurality of nodes and a plurality of edges, each of the nodes comprising members representative of at least one subset of training data points, each of the edges connecting nodes that share at least one data point, grouping the data points into a plurality of groups, each data point being a member of at least one group, creating a first transformation data set, the first transformation data set including the training data set as well as a plurality of feature subsets associated with at least one group, values of a particular data point for a particular feature subset for a particular group being based on values of the particular data point if the particular data point is a member of the particular group, and applying a machine learning model to the first transformation data set to generate a prediction model.
Owner:SYMPHONYAI SENSA LLC
Methods and systems of spatiotemporal pattern recognition for video content development
InactiveUS20170255829A1Facilitate decision-makingIncrease entertainmentImage enhancementTelevision system detailsRecognition algorithmVideo production
A system for enabling user interaction with video content includes an ingestion facility configured to access at least one video feed and a machine learning system configured to process the at least one video feed through a spatiotemporal pattern recognition algorithm that applies machine learning on an event in the at least one feed in order to develop an understanding of the event including identifying context information relating to the event and an entry in a relationship library at least detailing a relationship between two visible video features. The system further includes an extraction facility configured to automatically extract content displaying the event and associate the extracted content with the context information, and a video production facility configured to produce a video content data structure that includes the context information. The system further includes a user interface configured with video interaction options that are based on the context information.
Owner:GENIUS SPORTS SS LLC
Data extraction engine for structured, semi-structured and unstructured data with automated labeling and classification of data patterns or data elements therein, and corresponding method thereof
ActiveUS20180114142A1Easy to implementNovel level of efficiencyKernel methodsCharacter and pattern recognitionData ingestionFeature vector
A fully or semi-automated, integrated learning, labeling and classification system and method have closed, self-sustaining pattern recognition, labeling and classification operation, wherein unclassified data sets are selected and converted to an assembly of graphic and text data forming compound data sets that are to be classified. By means of feature vectors, which can be automatically generated, a machine learning classifier is trained for improving the classification operation of the automated system during training as a measure of the classification performance if the automated labeling and classification system is applied to unlabeled and unclassified data sets, and wherein unclassified data sets are classified automatically by applying the machine learning classifier of the system to the compound data set of the unclassified data sets.
Owner:SWISS REINSURANCE CO LTD
Product comment analyzing method and system with learning supervising function
InactiveCN105550269AReduce precisionMeet the needsData miningSpecial data processing applicationsData dredgingMultiple category
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.
Owner:FUDAN UNIV
Method and system for supervised learning of road signs
PendingUS20200050973A1Huge saving in timeHuge saving in costInstruments for road network navigationMachine learningGround truthSupervised learning
A method, system, and computer program product is provided, for example, for predicting a location of a road sign. In an example embodiment, the method may include receiving a set of pre-processed road observations and extracting a plurality of features from the set of pre-processed road observations, wherein the plurality of features comprise at least a plurality of sensor based features and a plurality of map based features. Further, the method may include associating a set of sensor based features from the plurality of sensor based features and a set of map based features from the plurality of map based features with at least one of a plurality of ground truth points in ground truth data. Additionally, the method may include training a machine learning model based on the association of the ground truth data with the set of sensor based features and the set of map based features with at least one of a plurality of ground truth points in the ground truth data and further predicting the location of the road sign based on the trained machine learning model.
Owner:HERE GLOBAL BV
Methods and systems of spatiotemporal pattern recognition for video content development
ActiveUS20170255826A1Facilitate decision-makingIncrease entertainmentTelevision system detailsImage enhancementEvent typeRecognition algorithm
Presenting event-specific video content that conforms to a user selection of an event type includes processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of at least one event within the at least one video feed to determine at least one event type, wherein the at least one event type includes an entry in a relationship library at least detailing a relationship between two visible features of the at least one video feed, extracting the video content displaying the at least one event and associating the understanding with the video content in a video content data structure. A user interface is configured to permit a user to indicate a preference for at least one event type that is used to retrieve and provide corresponding extracted video content with the data structure in a new video feed.
Owner:GENIUS SPORTS SS LLC
Methods and systems of spatiotemporal pattern recognition for video content development
ActiveUS20170255827A1Facilitate decision-makingIncrease entertainmentImage enhancementTelevision system detailsRecognition algorithmVideo game development
Owner:GENIUS SPORTS SS LLC
Methods and systems of spatiotemporal pattern recognition for video content development
ActiveUS20170255828A1Facilitate decision-makingIncrease entertainmentTelevision system detailsImage enhancementRecognition algorithmUser interface
Owner:GENIUS SPORTS SS LLC
Resource recommendation method and device and electronic equipment
ActiveCN110413877AImprove conversion rateRealize personalized recommendationDigital data information retrievalMachine learningPrediction scoreOperational behavior
The embodiment of the invention provides a resource recommendation method and device and electronic equipment. The method comprises the following steps: receiving a resource recommendation request ofa target user; according to the resource recommendation request, obtaining feature information of the target user and feature information of to-be-recommended resources associated with the target userfrom an online database, wherein the feature information of the to-be-recommended resources comprises information of multiple historical operation behavior data of the to-be-recommended resources; inputting the feature information of the target user and the feature information of the to-be-recommended resource into an online machine learning model to obtain a prediction score of the to-be-recommended resource; and selecting one or more to-be-recommended resources from the to-be-recommended resources according to the prediction score, and recommending the one or more to-be-recommended resources to the target user to complete resource recommendation for the target user.
Owner:ADVANCED NEW TECH CO LTD
Cloth defect detection method and device based on machine learning
InactiveCN107123114AMeeting Industrial NeedsAccurate detectionImage enhancementImage analysisPhysical hard workImage segmentation
The invention discloses a cloth defect detection method based on machine learning. The method particularly comprises the following steps of image segmentation, image enhancement, image denoising, sub image feature extraction, defect point area segmentation, offline cloth learning, online cloth detection, and cloth defect point classification. In the offline cloth learning stage, a BP neural network is used to train standard image characteristic parameters, and a standard value is obtained. In the online cloth detection stage, the BP neural network is used to detect the feature parameters of the sub image. In the cloth defect point classification stage, a depth learning algorithm based on a convolutional neural network is used to classify cloth defects. The cloth defect detection method based on machine learning has a self-learning function and can meet the continuously-developing industry needs. The invention also provides a cloth defect detection device based on machine learning, which comprises an image acquisition unit, an image processing unit, a data communication unit and an action execution unit. The detection device can realize high-efficiency and accurate detection, and workers can be freed from heavy and tasteless physical labor.
Owner:FOSHAN NANHAI GUANGDONG TECH UNIV CNC EQUIP COOP INNOVATION INST +1
CrowdChunk System, Method and Computer Program Product for Searching Summaries of Mobile Apps Reviews
System, method, and computer program product (e.g. mobile App) and / or web-based service to enable users to research reviews written online to assess the performance and functionality of mobile applications. The system extracts reviews from multiple online sources comprising: mobile Apps “stores”, blogs, online magazines, websites, etc.; and, utilizes sentiment analysis algorithms and supervised machine learning analysis to present more informative summaries for each App's reviews. Summaries may comprise: a sentence that encapsulates a sentiment held by many users; the most positive and negative comments; and a list of features with average scores (e.g. graphics, fun, easy to use, etc.). Additionally, the user may view a separate review detail page per App that provides further summaries, such as a short list of other Apps that the same reviewer gave a very positive review for the features. The user is then able to purchase and download the App via a link.
Owner:CODEQ +1
Vehicle type identification method based on support vector machine and used for earth inductor
InactiveCN102779281AImprove the efficiency of fusion processingImprove learning effectRoad vehicles traffic controlCharacter and pattern recognitionSupport vector machineInductor
The invention relates to a vehicle type identification method based on a support vector machine and used for an earth inductor. The vehicle type identification method includes the following steps: vehicle type waveform data which require to be identified are collected by the earth inductor; a plurality of numeralization features are extracted from waveforms, effective data are screened out, and the features are normalized; multilayer feature selection is performed according to the extracted features, and an optimal feature combination is picked out; a vehicle type classification algorithm based on the clustering support vector machine is built, and parameters in a classification function are optimized by adopting a particle swarm optimization algorithm; a binary tree classification mode is built, classifiers on all classification nodes are trained, and a complete classification decision tree is built; and earth induction waveforms of a vehicle type to be identified are input to obtain identification results of the vehicle type. The vehicle type identification method builds a waveform feature extraction and selection mode, simultaneously adopts the classification algorithm based on the support vector machine and the particle swarm optimization algorithm, greatly improves machine learning efficiency, and enables a machine to identify vehicle types rapidly and accurately.
Owner:TONGJI UNIV
Multi-mode Web application protection method based on Mshield machine learning
InactiveCN107404473ALow technical requirementsEfficient and more secure protection capabilitiesCharacter and pattern recognitionTransmissionData warehouseData set
The invention belongs to the technical field of the Web application protection, and discloses a multi-mode Web application protection method based on Mshield machine learning. The method comprises the following steps: extracting features through a semantic analysis unit, transmitting to a machine learning unit to recognize, and applying a random forest algorithm and a logic regression algorithm; effectively distinguishing hostile attack from a normal access request, and immediately intercepting the attack, wherein the Mshield cloud platform screens logs reported by the device everyday and them summarizes to place in a database as the iteration and a data set for improving the algorithm effect; performing deduction according to the past security posture, predicting the possible attack event and an application easy to be attacked in future. By acquiring the features of mega attack load data, through the training and the generalization of a machine learning model, the Mshield is more efficient and safer protection capacity in the face of the current even the unknown Web attack in future in comparison with the traditional WAF.
Owner:XIDIAN UNIV
Using artificial intelligence to design a product
ActiveUS20180349795A1Digital data information retrievalForecastingEngineeringArtificial intelligence
In an embodiment, a method for optimizing computer machine learning includes receiving an optimization goal. The optimization goal is used to search a database of base option candidates (BOC) to identify matching BOCs that at least in part matches the goal. A selection of a selected base option among the matching BOCs is received. Machine learning prediction model(s) are selected based at least in part on the goal to determine prediction values associated with alternative features for the selected base option, where the model(s) were trained using training data to at least identify weight values associated with the alternative features for models. Based on the prediction values, at least a portion of the alternative features is sorted to generate an ordered list. The ordered list is provided for use in manufacturing an alternative version of the selected base option with the alternative feature(s) in the ordered list.
Owner:STITCH FIX INC
Malicious URL detection method and implement system thereof
The invention discloses a malicious URL detection method and a system. The method comprises the steps of splitting a URL to be detected into character strings according to a URL grammar and a semantic structure in the RFC1738 standard; analyzing, completing and modifying the character strings which are obtained by splitting; aiming at a new URL formed by the processed character strings, traversing and matching a URL knowledge base; judging whether the new URL contains malicious features and belongs to a short URL according to rules, and if the new URL belongs to the short URL, restoring the short URL into a long URL; finally, extracting the features from the URL knowledge base, applying a sorting algorithm training model through machine learning, and predicting the malicious property of the URL. By applying the method and the system, the flexibility and eversporting property of URL forms are solved, a newly-emerging malicious website can be recognized, the harm from the malicious URL can be effectively resisted, and the safety performance of user information can be substantially improved.
Owner:JIANGSU PAYEGIS INFORMATION SECURITY TECH CO LTD
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