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79 results about "Multiclass classification" patented technology
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In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of three or more classes. (Classifying instances into one of two classes is called binary classification.)
System and method for multiclass classification of images using a programmable light source
An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.
Owner:VERILY LIFE SCI LLC
Learning A Coarse-To-Fine Matching Pursuit For Fast Point Search In Images Or Volumetric Data Using Multi-Class Classification
A landmark location system for locating landmarks in volumes includes a medical image database including volumes of medical images, a learning unit that trains a multi-class classifier to locate a landmark point in each volume from extracted features of the volumes near a sample point offset from the landmark point and discrete displacements of the sample point to the landmark point, and a landmark locator that locates the landmark point in an input volume using the trained multi-class classifiers.
Owner:SIEMENS HEALTHCARE GMBH
Stacking schema for classification tasks
ActiveUS20090182696A1Improving stacking schemaEnsemble classifiers are improvedEnsemble learningDigital computer detailsClass modelBinary classification
a method for improving stacking schema for classification tasks, according to which predictive models are built, based on stacked-generalization meta-classifiers. Classifications are combined to build a new scheme from at least two layers and multiclass classification problems are converted into binary classification problems. One-against-all class binarization and regression learners are used for each class model and ensemble classifiers are improved using stacking. Accuracy differences, accuracy ratio, and runtime classification in multiclass datasets are also improved and the class of a value is then predicted.
Owner:DEUTSCHE TELEKOM AG
Semantic classification method for Chinese question
InactiveCN1804829AAvoid problems that affect classification accuracyImprove accuracySpecial data processing applicationsNatural language processingFeature vector
The invention discloses a semantic category method of Chinese problem based on Chinese problem semantic category system and Chinese problem multi-type category model. It is formed by Chinese problem semantic category system constructing, Chinese problem character vector representation and Chinese problem multi-type category based on uniformity loss; the Chinese problem semantic category system constructs a problem semantic category system including the definition and the quoting method of all of the semantic category, the problem category result is one of the problem semantic category; the Chinese problem character vector uses some characters to represent the problem text into the character vector which the category model can identify; the Chinese problem multi-type category based on uniformity loss maps the inputted problem character vector to one category of the problem semantic category system.
Owner:XI AN JIAOTONG UNIV
Image classification method and image classification device
InactiveCN101877064AAccurate classificationEfficient retrievalCharacter and pattern recognitionFeature extractionClassification methods
The invention discloses an image classification method and an image classification device. The image classification method comprises the following steps: image segmentation: segmenting inputted image into a plurality of areas; feature extraction: extracting visual feature of each area; a first classification: base on the extracted visual feature of each area, using a first classifier to carry out multiclass classification for the area according to preset multiclass; context feature generation: generating context feature of the image based on classification result of each area; and a second classification: based on the generated context feature of the image, determining image category with a second classifier. According to the technical scheme of the invention, image classification performance can be effectively improved by utilizing context information of images, without displaying space relation among objects in defined mages.
Owner:SONY CORP +1
Inquiry-related multi-ranking-model integration algorithm
InactiveCN102043776AImprove performanceSpecial data processing applicationsAlgorithmDocument preparation
The invention discloses a brand new inquiry-related multi-ranking-model integration method. The brand new inquiry-related multi-ranking-model integration method comprises the following steps of: establishing sub-ranking models for each inquiry and inquiry-related document; performing vectoring representation on the sub-ranking models so as to convert a plurality of inquiry-related ranking models into characteristic data and integrate the plurality of ranking models; establishing a new loss function as an optimization target at an inquiry level and a sample level by using a ranking support vector machine as the sub-ranking model; adjusting weight among losses generated by different inquiries by using the loss function; and providing a multiple inquiry-related ranking support vector machine fusion algorithm. Compared with the traditional model, the inquiry-related ranking models can achieve better properties when the inquiry-related multi-ranking-model integration algorithm provided by the invention is applied to actual tasks. The multi-model fusion algorithm provided by the invention can be applied to ranking learning, can also be applied to multi-element classification, sequence labeling and the like, and has a wide application prospect in the fields of information retrieval, network search and the like.
Owner:NANKAI UNIV
Method for searching new position of feature point using support vector processor multiclass classifier
InactiveCN1776711AHigh precisionCharacter and pattern recognitionSupport vector machineImage processing
The method includes steps: (1) building ASM model, and initializing ASM model so as to obtain initial position of model; (2) generating multiple classes of training samples corresponding to each feature point on human face in order to train classifier in multiple classes of vector machine; (3) using training sample corresponding to each feature point trains the train classifier in multiple classes of vector machine; (4) using initial position of model as initial position of ASM searching, and using the said classifier to locate each feature point. The method is related to detecting eye, training classifier, converting classifier in two classes to classifier in multiple classes, and ASM location for feature point. Further, the invention is applicable to recognition of human face, recognition of sexuality etc. possessing high precision.
Owner:SHANGHAI JIAO TONG UNIV
Machine learning based vehicle logo identification method
ActiveCN105740886AEasy to useImprove classification accuracyThree-dimensional object recognitionLearning basedPattern recognition
The present invention provides a machine learning based vehicle logo identification method. The method comprises the following steps: 1) performing vehicle logo location: searching out a vehicle license plate according to a vehicle license plate location method, and on this basis, determining a region where a vehicle logo is possibly located, i.e. an ROI region as a vehicle logo location image; 2) extracting an SIFT feature set of the vehicle logo location image; 3) by using a clustering method, generating K keywords and a weight of each keyword from the SIFT feature set; 4) obtaining the K keywords and the weight of each keyword from the SIFT feature set as an input of a classifier; and 50 performing classification by using a well-trained multi-class SVM classifier. The present invention provides a machine learning based vehicle logo identification method that reduces time costs, is convenient for use and is high in accuracy.
Owner:杭州熵领科技有限公司
Method for automatically classifying three-dimensional models based on support vector machine
InactiveCN102622609AAchieve outputImprove robustnessCharacter and pattern recognitionFeature extractionDistance matrix
The invention discloses a method for automatically classifying three-dimensional models based on a support vector machine. The method comprises the steps as follows: carrying out dimension reduction and characteristic decomposition on a geodesic distance matrix; creating binary classifiers for the characteristics of the three-dimensional models by using the support vector machine; and combining every two of the binary classifiers in a 'one-to-one' way by using the support vector machine to form a polynary classifier. According to the method, the three-dimensional models can be automatically classified, so that the robustness of the characteristic extraction process of the models is higher, the computing speed is higher, the characteristic extraction time is greatly shortened, and fewer three-dimension model training sample conditions can be well corresponded. The method has higher generalization performance and good expansion capability and non-linearity performance.
Owner:HAIAN JULI MAGNETIC MATERIAL CO LTD +1
Breast ultrasonic image multi-classification system and method based on cross correlation characteristics
ActiveCN107358267AImprove auxiliary diagnosis effectMeet needsImage enhancementImage analysisFeature vectorSonification
The invention provides a breast ultrasonic image multi-classification system and method based on cross correlation characteristics. The system comprises an ultrasonic image preprocessing unit; an interest area extraction unit used for extracting an image of an interest area; an internal cross correlation density feature extraction unit used for extracting an internal cross correlation density characteristic value of an interest image; a conventional feature extraction unit used for extracting a plurality of conventional characteristic values of the interest area image; and a multi-classification unit used for training classifiers, inputting an internal cross correlation density characteristic value vector and conventional characteristic vectors to three trained classifiers for classification, and regarding the category with the most prediction as a final classification result. According to the classification method, the internal cross correlation density characteristic based on the interest area is additionally provided, the auxiliary diagnosis effect of a breast ultrasonic computer can be effectively improved, the classification category of galactocele which is the benign lesion is added, and the requirement of a breast ultrasonic computer auxiliary system by doctors is further met.
Owner:NORTHEASTERN UNIV
Turbulence target detection method based on BP neural network multi-class classification
ActiveCN108710114AEffective target detectionEstablish a non-linear relationshipWave based measurement systemsOne-class classificationSignal-to-noise ratio (imaging)
The invention provides a turbulence target detection method based on BP neural network multi-class classification. The method comprises the step of according to a turbulence echo mode, under differentsignal to noise ratio conditions, generating echo amplitude sequences, and training sets and test sets of a BP neural network formed by corresponding strength grades so as to allow the trained BP neural network to divide the turbulence intensity into multiple grades, thereby achieving turbulence detection. According to the invention, an experience formulas and parameterization models are not required, by use of the multi-class classification function of the neural network, the echo amplitude sequences of a weather target radar are used as input data of the training set of the neural network;the turbulence intensity grades are used as output data of the training set; the relation between the radar echoes and turbulence intensity can be effectively determined only through learning of massecho data; and an objective that turbulence detection is achieved by use of the neural network to classify the turbulence intensities can be achieved.
Owner:SHANGHAI JIAO TONG UNIV
Methods to distribute multi-class classification learning on several processors
ActiveUS7552098B1Increase the speed of learningKernel methodsDigital computer detailsLanguage understandingOne-class classification
The time taken to learn a model from training examples is often unacceptable. For instance, training language understanding models with Adaboost or SVMs can take weeks or longer based on numerous training examples. Parallelization thought the use of multiple processors may improve learning speed. The invention describes effective methods to distributed multiclass classification learning on several processors. These methods are applicable to multiclass models where the training process may be split into training of independent binary classifiers.
Owner:NUANCE COMM INC
Method and system for detecting local impedance change of objects
InactiveCN102590348AAccurate quantitative identificationEasy to useAnalysing solids using sonic/ultrasonic/infrasonic wavesMeasurement of torque/twisting force while tighteningPattern recognitionObject based
The invention discloses a method and a system for detecting local impedance change of objects based on controlled knocking and a classifier. Classification is carried out by that an object has different local impedances and different response signals after being knocked and by the combination with a pattern recognition method. The method and the system not only can be used for detecting bolt screwing moment, but also can be used for detecting the damage status of composite material. The device adopted by the method is simpler, the method is simple and practical, the detection result is accurate, and multiclass classification can be realized.
Owner:TSINGHUA UNIV
Stacking schema for classification tasks
ActiveUS8244652B2Improving stacking schemaEnsemble classifiers are improvedEnsemble learningDigital computer detailsClass modelAlgorithm
A method for improving stacking schema for classification tasks, according to which predictive models are built, based on stacked-generalization meta-classifiers. Classifications are combined to build a new scheme from at least two layers and multiclass classification problems are converted into binary classification problems. One-against-all class binarization and regression learners are used for each class model and ensemble classifiers are improved using stacking. Accuracy differences, accuracy ratio, and runtime classification in multiclass datasets are also improved and the class of a value is then predicted.
Owner:DEUTSCHE TELEKOM AG
Cyberspace threat knowledge extraction method and device
ActiveCN111597353AExtract solutionCharacter and pattern recognitionSpecial data processing applicationsMulti-label classificationKnowledge extraction
The invention relates to a cyberspace threat knowledge extraction method and device. The cyberspace threat knowledge extraction method comprises the steps of extracting ontology concepts of a to-be-extracted text and relationships among the ontology concepts according to a pre-trained multi-label classifier; extracting examples corresponding to the ontology concepts in the text to be extracted according to a pre-trained multi-class classifier of each ontology concept; and according to the entities corresponding to the ontology concepts and the relationship between the ontology concepts, determining the relationship between the instances. By adopting the cyberspace threat knowledge extraction method, the extraction problem of cyberspace threat knowledge in the unstructured text can be wellsolved by combining the hidden entity and the ontology relationship.
Owner:NAT UNIV OF DEFENSE TECH
Method for retrieving information by multiclass classification
The invention discloses a method for retrieving information by multiclass classification. One index property is defined in each piece of information; meanwhile, a plurality of index properties of same index condition are predefined for a searcher; and during retrieval, multiple pieces of information satisfying different index attribute requirements can be retrieved once without inputting a retrieval condition. The method can be used for retrieving purchase or supply information for the Internet e-commerce and quickly displaying retrieval results while omitting or reducing operation on retrieval for users, thereby having a positive meaning on popularization and application for the e-commerce in the practical commercial activity.
Owner:范煜
Combining multiclass classifiers with regular expression based binary classifiers
A computer device may include a memory storing instructions and processor configured to execute the instructions to obtain a text file generated based on a communication session; select topics for classifying communication sessions; and apply one or more trained machine learning models to the obtained text file to determine one or more most likely topics for the communication session. The processor may be further configured to apply regular expression binary classifiers, each associated with a different topic, to the obtained text file to determine a likelihood the communication session is associated with a particular topic; select a topic for the communication session based on the determined one or more most likely topics and the determined likelihood the communication session is associated with the particular topic; and classify the communication session based on the selected topic.
Owner:VERIZON PATENT & LICENSING INC
Motion analysis method, device and equipment and storage medium
PendingCN111860157AImprove accuracyImprove recognition accuracyCharacter and pattern recognitionHuman bodyMedicine
The invention relates to a motion analysis method, which comprises the steps of obtaining a first motion set sequence of a trainer according to a human body posture estimation algorithm OpenPose and amotion video of the trainer; obtaining a one-dimensional action feature vector and a two-dimensional action feature point set of the action video according to the first action set sequence; obtainingaction classification of the action video according to the one-dimensional action feature vector and a multi-class classifier; According to the standard two-dimensional action feature point set corresponding to the action classification and the two-dimensional action feature point set, obtaining an action score of the trainee. According to the method, large-scale computing resources are not needed, and the accuracy of fitness action recognition and evaluation is improved. The invention further relates to a motion analysis device, a storage medium and equipment.
Owner:BEIJING SPORT UNIV
Multi-moving-object feature expressing method suitable for different scenes
InactiveCN103279769ADecrease in invariant moment valueShorten the timeCharacter and pattern recognitionVideo monitoringComputer vision
The invention discloses a multi-moving-object feature expressing method suitable for different scenes. Aiming at the feature that different movable objects are different in characteristic, a self-adaption combined invariant moment value method is provided, and an invariant moment value which is selected in a dynamic mode is used for describing the characteristics of different moving objects. Through a definition similar frequency-opposite singular frequency method, namely, an SF-ISF method, a weighted value of the invariant moment value of each object is calculated, and then the weighted value of the invariant moment value and a combined invariant moment value are used as input parameters; multiple types of classifier models are established and classification is conducted on various moving objects in a scene. The multi-moving-object feature expressing method suitable for different scenes can effectively reduce computation time, is high in rate of identification of the moving objects, suitable for being used for identifying the moving objects which are monitored in real time, and applicable to various video monitoring scenes.
Owner:JIANGSU UNIV
Text classification method based on representation enhancement and fusion
PendingCN111813939AApproximate the true distributionDistribution reservedCharacter and pattern recognitionNeural architecturesFeature vectorDatasheet
The invention relates to a text classification method based on representation enhancement and fusion. The method comprises the steps that a text classification model based on representation enhancement and fusion is constructed, and the processing steps of an input text in the text classification model based on representation enhancement and fusion are as follows: discrete characters of the inputtext are converted into continuous feature vectors in a data representation layer to obtain multiple representation vectors; adding disturbance into the representation vector in the representation enhancement layer to obtain a representation enhancement vector; further extracting and abstracting the characterization enhancement vector in the characterization abstraction layer to obtain an abstractcharacterization vector; classifying the abstract representation vectors in a classification layer to obtain output text tags; and synthesizing each output text label in the fusion layer to obtain afinal text label. The method can effectively solve the problems that in existing text multi-class classification, distribution of sample data among classes is unbalanced, and correct classification isdifficult when the number of samples in a small number of classes is insufficient.
Owner:南京睿晖数据技术有限公司 +1
ZPW-2000A type uninsulated track circuit fault diagnosis method
ActiveCN110852365AImprove fault diagnosis efficiencyStable diagnostic performanceCharacter and pattern recognitionNeural learning methodsData ingestionTest sample
The invention provides a ZPW-2000A type uninsulated track circuit fault diagnosis method based on a DBM-SVM. The ZPW-2000A type uninsulated track circuit fault diagnosis method based on the DBM-SVM isused for solving the problems of randomness, low diagnosis precision, large error and long period of manual detection. The diagnosis method comprises the following steps: dividing original data intoa training sample set and a test sample set; taking training sample set data as input data of the DBN; extracting features of track circuit fault original data, training a binary tree type support vector machine multi-class classifier by adopting the extracted features of the track circuit fault original data, and sending a test sample set into the binary tree type support vector machine multi-class classifier for classification to obtain a diagnosis result. According to the invention, based on the DBM-SVM model, fault diagnosis is carried out on the ZPW-2000A uninsulated track circuit, the diagnosis performance is stable, the precision is high, the error is small, the work period is short, and the fault diagnosis efficiency of the uninsulated track circuit is improved.
Owner:BEIJING JIAOTONG UNIV
Device and method for multi-class classification by machine learning
ActiveUS20200050964A1Minimizes stepSimple technologyEnsemble learningCharacter and pattern recognitionPattern recognitionAlgorithm
A method and a device for multi-class classification of an application relative to a classification of a transport mode by machine learning, including: (a) sensors configured to measure at successive instants physical quantities specific to said application, and (b) a microprocessor configured to: (b1) acquire said successive measurements from the sensors to calculate predictors from physical quantities of said measurements, (b2) construct continuous series (aj) of samples, each sample being a vector formed of a predetermined number of predictors, (b3) break down each series (aj) of samples into a set of sequences (Sk) each corresponding to a specific class among a set of predetermined classes, (b4) under-sample the sequences associated with the classes among said set of predetermined classes while conserving for each selected sequence a predetermined fraction of samples of said sequence, thereby forming classes associated with balanced samples, and (b5) construct a classifier (F) from said balanced samples by machine learning.
Owner:COMMISSARIAT A LENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
Protein structure class prediction method based on weighted composition position vectors and support vector machine
InactiveCN105205343ARepresentation rich in informationImprove forecast accuracySpecial data processing applicationsData setAlgorithm
Owner:SYSU CMU SHUNDE INT JOINT RES INST +1
Method for multiclass classification in open-set scenarios and uses thereof
ActiveUS10133988B2Increase valuePromote resultsKernel methodsCharacter and pattern recognitionTraining phaseTest sample
The proposed method is used for classification in open-set scenarios, wherein often it is not possible to first obtain the training data for all possible classes that may arise during the testing stage. During the test phase, test samples belonging to one of the classes used in the training phase are classified based on a ratio between similarity scores, as known correct class and test samples belonging to any other class are to be rejected and classified as unknown.
Owner:SAMSUNG ELECTRONICSA AMAZONIA LTDA +1
System and method for multiclass classification of images using a programmable light source
An apparatus, system and process for identifying one or more different tissue types are described. The method may include applying a configuration to one or more programmable light sources of an imaging system, where the configuration is obtained from a machine learning model trained to distinguish between the one or more different tissue types captured in image data. The method may also include illuminating a scene with the configured one or more programmable light sources, and capturing image data that includes one or more types of tissue depicted in the image data. Furthermore, the method may include analyzing color information in the captured image data with the machine learning model to identify at least one of the one or more different tissue types in the image data, and rendering a visualization of the scene from the captured image data that visually differentiates tissue types in the visualization.
Owner:VERILY LIFE SCI LLC
Multiclass classification method for the estimation of eeg signal quality
PendingUS20210267530A1Precise positioningAccurate informationMedical automated diagnosisDiagnostic recording/measuringPattern recognitionSignal quality
A method for assessing an electroencephalographic signal quality based on a multiclass classification, including: receiving at least one segment of electroencephalographic signal from at least one electrode; extracting at least one feature value from each electroencephalographic signal segment channel; classifying with a first classification to assign each electroencephalographic signal segment channel to one of at least three quality classes. The first classification is performed by a k-nearest neighbors' algorithm: using a first training set of multiple training samples, each training sample being associated to a quality class and to at least one feature value; and assigning to each electroencephalographic signal segment channel the quality class which is the most frequent class among the training samples of the first training set nearer to each electroencephalographic signal segment channel; the distance is calculated between the feature value of each electroencephalographic signal segment channel and each feature value of the training samples.
Owner:MYBRAIN TECH
A data classification method and apparatus
InactiveCN109543771AFully consider random factorsClear classification logic structureCharacter and pattern recognitionData setAlgorithm
The invention discloses a data classification method, which comprises the following steps: determining the probability that a prediction data set respectively belongs to each category; Constructing probability intervals belonging to each category of the prediction data set according to the probability; randomly generating a random number between [0, 1] is randomly generated, and determining a category of the prediction data set according to a probability interval in which the random number is located. A data classification apparatus is also disclosed. This scheme is mainly applied to the multi-class classification problem in machine learning field. This method not only enriches the internal logical structure of multi-class logistic regression algorithm, but also enhances the performance of multi-class logistic regression algorithm by judging the class and fully considering the influence of training sample error.
Owner:ZHENGZHOU YUNHAI INFORMATION TECH CO LTD
System, method, apparatus and computer program product for the detection and classification of different types of skin lesions
A system, method, apparatus and computer program product for the detection and classification of different types of skin lesions that leverages artificial intelligence (AI) is disclosed. SkinScreen® uses a novel approach that we have labeled as ‘serial chain classifiers’. This approach uses a binary classifier, to determine whether a skin lesion is present in the image, then if a lesion is present uses a multi-class classifier to classify the type of skin lesion. This approach removes manual human intervention in the process that is employed by current solutions while improving the accuracy and precision of the results. Using novel techniques of image transformation, the datasets used to train the AI models were expanded by a factor of 8. The larger the dataset, the more accurate and precise the results. These novel approaches have resulted in a better screening detection tool.
Owner:BENKERT JASON TROY
Semantic segmentation with soft cross-entropy loss
A system and method for semantic segmentation with a soft cross-entropy loss is provided. The system inputs a first color image to an input layer of a semantic segmentation network for a multi-class classification task. The semantic segmentation network generates, at an auxiliary stride, a first feature map as an output of an auxiliary layer of the semantic segmentation network based on the input first color image. The system extracts the generated first feature map from the auxiliary layer and computes a probability map as a set of soft labels over a set of classes of the multi-class classification task, based on the extracted first feature map. The system further computes an auxiliary cross-entropy loss between the computed probability map and a ground truth probability map for the auxiliary stride and trains the semantic segmentation network for the multi-class classification task based on the computed auxiliary cross-entropy loss.
Owner:SONY CORP
Classification system
Owner:GOOGLE LLC
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