172 results about "Class model" patented technology

The invention discloses a code generation method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: screening out a target reusable code meta-model corresponding to code generation demand information from a pre-constructed code model library according to the code generation demand information; the code model library comprises reusable code meta-models corresponding to various businesses, and the reusable code meta-models comprise one or more of an application template class model, a function module class model and a code snippet class model; verifying the target reusable code meta-model by using a pre-constructed rule base to obtain a verified target reusable code meta-model; and combining all the target reusable code meta-models passing the verification to obtain a program code corresponding to the code generation demand information. On the basis of ensuring the accuracy of code generation, the generation of multi-field program codes can be realized, and the universality of code generation in the application field is improved.

A problem of imbalanced big data is solved by decoupling a classifier into a neural network for generation of representation vectors and into a classification model for operating on the representation vectors. The neural network and the classification model act as a mapper classifier. The neural network is trained with an unsupervised algorithm and the classification model is trained with a supervised active learning loop. An acquisition function is used in the supervised active learning loop to speed arrival at an accurate classification performance, improving data efficiency. The accuracy of the hybrid classifier is similar to or exceeds the accuracy of comparative classifiers in all aspects. In some embodiments, big data includes an imbalance of more than 10:1 in image classes. The hybrid classifier reduces labor and improves efficiency needed to arrive at an accurate classification performance, and improves recognition of previously-unrecognized images.

A multi-class classification system performs multi-class classification using an adapted, multi-level binary classification model in conjunction with pre-classification and post-classification processing. Original, multi-class training data is transformed into coarse-level adapted training data and fine-level adapted training data. In the coarse-level adapted training data, objects from the original training set are classified into either a majority classification set (representing the largest original classification set) or a non-majority classification set representing a combination of the remaining classification sets from the original training data. In the fine-level adapted training data, objects from the non-majority classification set retain their original classifications from the original training data. The adapted training data is used to train coarse-level and fine-level instances of the binary classification model, which are used to generate coarse-level and fine-level binary classifications of objects with respect to an input data set. The binary classifications are transformed into a multi-class classification.

The invention discloses a teaching quality evaluation and optimization platform based on OpenPose skeleton key point detection. The teaching quality evaluation and optimization platform comprises a classroom behavior recognition module, a teaching quality evaluation module and a teaching analysis and optimization module. The classroom behavior recognition module acquires and preprocesses video information generated in a classroom video, then detects skeleton key points in the classroom video by using an OpenPose skeleton key point detection model to recognize teacher and student classroom behaviors, and then performs training based on a support vector machine classification algorithm to generate a classroom behavior classification model to classify the teacher and student classroom behaviors; the teaching quality evaluation module is used for calculating the class positive degree, the class attending seriousness of the students and the teaching seriousness of the teachers and evaluating the teaching quality; the teaching analysis and optimization module carries out teaching analysis and optimization based on teacher and student classroom behaviors and teaching quality evaluation. According to the invention, classroom behaviors of teachers and students can be identified and classified in real time, and comprehensive, objective and reliable teaching quality evaluation is provided.

The invention provides a rheumatoid arthritis patient low muscle quality risk prediction method based on an uncertainty perception stacked meta-learning structure, and belongs to the technical field of machine learning. The method comprises the following steps: acquiring a rheumatoid arthritis data set; constructing a base learner set comprising a table Transform class network and a gradient boosting tree class model; splicing the out-of-fold prediction probabilities of all the base learners and the statistics thereof to form a meta-feature matrix; taking the meta feature matrix as input, and constructing and training a meta learner comprising a spectrum normalization multilayer perceptron and a random feature Gaussian process output layer; collecting to-be-detected data, inputting the to-be-detected data into the trained base learner set and the trained meta learner in sequence, and performing temperature scaling and beta-calibration on a prediction result; and taking the calibrated prediction probability as a final prediction result. Through multi-source data fusion, the defects of an existing model in the aspects of practicability, probability reliability and the like are overcome.

The invention discloses a classification model training method, a related method, a device, equipment and a medium. Comprising the steps of obtaining an unknown prediction result, a newly-added prediction result and a known prediction result for sample features of a first sample object based on a preset class increasing model; determining classification loss according to a target classification result and a category label obtained by classification of the unknown prediction result, the newly added prediction result and the known prediction result; a class increasing classification model is obtained through classification loss training; inputting the second sample object into the known class classification model and the increased class classification model, and optimizing the known class classification result according to the increased class classification result and the correction parameter; and determining fine tuning loss according to the known class optimization result, the increased class classification result and the class label, performing fine tuning on the correction parameter according to the fine tuning loss to obtain a fine-tuned correction parameter, and obtaining a target classification model based on the fine-tuned correction parameter, the known class classification model and the increased class classification model. According to the method, the classification accuracy can be still improved under the condition that the classification categories are newly added.

The invention provides a coding method and system of a Transform class model for channel state information prediction. The coding method comprises the following steps: S1, acquiring and processing channel state time sequence data; s2, inputting the processed data into an encoder to extract features; s3, CFR data needing to be predicted are embedded, and position codes of the model are added; decoding the relevant parameters using a decoder; s4, processing the output of the decoder, and predicting channel state information; and S5, taking the CFR data and the CSI data obtained in the actual scene as the input of the model, and predicting channel frequency response information. According to the method, the time sequence characteristics of the channel state information are calculated and coded, and the time information is brought into the model, so that the relative stability time of the channel at the future moment is considered, the problem of weak time sequence information embedding capability in a Transform model is solved, and the accuracy of channel state prediction is improved.

The embodiment of the invention provides a method for training a user classification model. The method comprises the following steps: acquiring a first classification model and a second classification model which are respectively initialized based on a pre-training model; performing class cluster prediction on each user in the first subset of the user set by using the first classification model to obtain a class cluster pseudo tag; and performing classification prediction of M user groups on each user by using the second classification model to obtain a classification pseudo tag. And determining a first user of an unknown category from the first subset according to the class cluster pseudo tag and the classification pseudo tag of each user. Respectively predicting the enhanced sample of the first user by using a first / second classification model to obtain a first / second prediction result; updating the first classification model by taking the first prediction result close to the class cluster pseudo label of the first user as a target; and updating the second classification model by taking maximization of the sum of the prediction probabilities corresponding to the M user groups in the second prediction result as a target.

The invention relates to the field of spine detection, and provides a scoliosis classification model training method, a scoliosis classification method and a scoliosis classification system in order to reduce computing resources and improve detection precision, and the scoliosis classification model is trained by constructing a class-balanced metadata set and a class-unbalanced basic data set. The model is guided to learn features of each category, the problem of category imbalance in a scoliosis data set is solved, and the generalization ability of the model under different scoliosis angle categories is effectively improved, so that the detection precision is improved; a Mama structure and an attention mechanism are introduced into the scoliosis classification model, and by optimizing a calculation process and a feature extraction mode, on the premise that the detection precision is guaranteed, the requirement for calculation resources is reduced, and the detection method can be effectively operated on common equipment.

The invention relates to the field of joint simulation of multi-class models, in particular to a joint simulation method of embedded computer digital twin multi-class models, which comprises the following steps: acquiring configuration information for a simulation object; obtaining a plurality of types of models required by simulation, packaging each type of model, constructing a to-be-operated simulation model library, and synchronously analyzing each type of model; starting a simulation model library, performing synchronous simulation, and determining resource synchronous competition characterization parameters for the synchronous simulation process based on complexity differences and load competition rates of various types of models so as to divide resource synchronous competition categories for the synchronous simulation process; in the synchronous simulation process, simulation parameters are adaptively controlled based on resource synchronous competition categories; according to the multi-class model joint simulation system and method, system resource competition in the multi-class model joint simulation process can be relieved, resource balanced distribution and the stability of the simulation process are guaranteed, and the simulation effect and efficiency are improved.

The present application relates to the field of artificial intelligence technology, and discloses a training method for a classification model, a search method for hyperparameters, and a device. The training method includes obtaining target hyperparameters of a classification model to be trained, where the target hyperparameters are used to control the gradient update step size of the classification model to be trained, and the classification model to be trained includes a scale-invariant linear layer, and the scale-invariant linear layer enables the prediction classification result output when the weight parameters of the classification model to be trained are multiplied by any scaling factor to remain unchanged; updating the weight parameters of the classification model to be trained according to the target hyperparameters and a target training method to obtain a trained classification model, and the target training method enables the magnitudes of the weight parameters of the classification model to be trained before and after updating to be the same. Based on the technical solution of the present application, it is possible to reduce the computing resources consumed for training the classification model while ensuring the accuracy of the classification model.

Certain example embodiments relate to meta-learning based error detection. Base classifiers are provided for historical attributes in historical datasets. Each is trained to indicate dirtiness of a value for the associated historical attribute. Clusters and a clustering model are generated using historical clustering features determined for each historical attribute, which are then associated with the clusters. For each dirty attribute in a dirty dataset, corresponding dirty clustering features are determined. The dirty attributes are assigned to the clusters using the corresponding determined dirty clustering features and the clustering model. The base classifiers associated with the clusters to which the dirty attributes were assigned are retrieved. Dirty features are extracted from the dirty dataset, and selectively modified. The extracted dirty features are applied to the retrieved the base classifiers to determine meta-features. A meta-classifier is trained using labeled meta-features. Predictions about the dirty dataset's dirtiness can be made using the meta-classifier.

The invention discloses a multi-document question and answer retrieval method combining a generative language model and a semantic document atlas, which further perfects and optimizes the construction and traversal mode of a document knowledge atlas, and respectively designs a bert-class model-based atlas construction and a KGP3 algorithm-based retrieval traversal mode optimization. In the graph traversal process, a main problem and an obtained node are combined, a sub-problem related to next needed node information is generated, the most appropriate document node is accurately selected from neighbor nodes, the correlation between the node and an initial problem is judged, and if the node and the initial problem are not related, the node and the initial problem are not listed in a follow-up retrieval list. The method is efficient, has traceability and interpretability, and abstractly summarizes the whole process of selecting the next hop node in graph traversal by the model. In order to reduce the cost, the small encoder-decoder T5 model is improved, and a contrast learning mechanism is introduced into the model, so that the retrieval performance of the small model is further improved.

Apparatus and associated methods relate to emergency stroke detection and classification. In an illustrative example, a stroke detection device may include an ensemble stroke classification model (ESCM). The ESCM may, for example, include class-specific model sets applicable for at least four classes of features, and a general model set applicable for all classes of features. Each model set, for example, may be stacked with multiple class-specific models for each of a corresponding group of architectures. The stroke detection device may, for example, extract predetermined features from a rolling window of a first predetermined duration of EEG data. The predetermined features are extracted and combined into a 1-D input vector. By applying the input vector, the stroke detection device may generate a binary stroke prediction result. Various embodiments may advantageously accurately predict whether a patient is experiencing a stroke within a finite time to assist an emergency service personnel.

The invention discloses a character recognition method and device based on deep learning and a storage medium, training is carried out through a training set to obtain a detection classification model, a character detection model and a character recognition model, and the detection classification model comprises a large class classification model and a subdivision classification model corresponding to each large class. The applicability can be improved through decoupling use, and the accuracy can be improved through combination of the two; the character detection network takes a deep convolutional neural network as a trunk network and is combined with a pixel aggregation network, so that high-layer and low-layer features have strong spatial and semantic information, and the character recognition network performs training according to CTC loss and Center Loss to improve the training speed and the recognition effect; the to-be-analyzed object is classified through the detection classification model to determine the target classification, the to-be-analyzed object is detected through the character detection network to determine the target character area, and the target character area is recognized through the character recognition model, so that the accuracy of a character recognition result is improved.

The application relates to a classification model training method, an object classification method and related equipment. The classification model training method comprises the following steps: obtaining a training sample set; the training sample set comprises a plurality of sample subsets, and the plurality of sample subsets correspond to a plurality of different kinds of category labels; a preset machine learning model is constructed; the category of each training sample in the training sample set is predicted based on the preset machine learning model, and prediction category information of each training sample is obtained; the prediction category information is determined based on the number of samples in each sample subset in the plurality of sample subsets and a feature output index of the training sample; a loss value is determined based on the prediction category information of each training sample and the category label of each training sample; the preset machine learning model is trained according to the loss value until a preset training end condition is met, and a classification model is obtained. In this way, the recognition accuracy of the classification model can be improved, and the safety of the application of the classification model in the automobile field can be improved.

The present application relates to a model training method, a feature classification method, an apparatus and a computer device. The method includes: the computer device determines the contribution of each symptom feature to each department based on the historical symptom feature data of multiple users, determines a sample data set based on the contribution of the historical symptom features, trains the initial classification model based on the sample data set, and obtains a target classification model; wherein the contribution is the frequency of occurrence of the symptom feature for the department, the initial classification model includes a first-layer classification model and a second-layer classification model, wherein the second-layer classification model is a least squares dual support vector machine meta-classification model, and the target classification model is used to classify departments according to the user's symptom features. The target classification model involved in this solution uses the least squares dual support vector machine as the second-layer meta-classifier of Stacking ensemble learning, which reduces the learning difficulty of the model during training and improves the solution efficiency of model training.

The invention provides a question reply method and device and electronic equipment, and relates to the technical field of artificial intelligence such as human-computer interaction, natural language understanding, large models and light weight. The method comprises the steps of obtaining a to-be-processed question input by a user in a current session; a plurality of alternative models associated with the to-be-processed problem are utilized to process the to-be-processed problem in parallel to obtain a plurality of candidate replies, the alternative models comprise at least one first-class model and at least one second-class model, and the parameter scale and / or single call cost of the first-class model are / is lower than that of the second-class model; determining a target model in the plurality of alternative models according to behavior information of the user for the plurality of candidate replies; and during the duration of the current session, generating corresponding reply contents for the input subsequent questions by using the target model. According to the method, the target model conforming to user preferences can be rapidly and clearly determined by simultaneously presenting the behavior information of the multiple candidate replies to the multiple alternative models by the user, and the interaction turns and the clarification cost are reduced.

The invention relates to an audio classification method, a model training method, a system, equipment and a medium. The training method comprises the following steps: acquiring an extended audio set and an audio tag set corresponding to the extended audio set; inputting the extended audio set into the trained original model, and extracting generated reference original category labels and original features; inputting the extended audio set into an extended model, and extracting a generated original category label and / or a newly added category label and an extended feature generated by the same network layer as the original model; and updating parameters of an expansion model based on the difference degree between the original category label and the reference original category label, the difference degree between the expansion feature and the original feature, and the difference degree between the original category label, the newly added category label and the corresponding audio label in the audio label set, and taking the trained expansion model as an audio classification model. According to the method, the problem of knowledge forgetting possibly occurring when the extension model learns a new category is solved, and meanwhile, the learning ability of the extension model to a new category is enhanced.

This specification discloses a distillation method for the fine-tuning stage of a text-based model, comprising: acquiring training set samples and a pre-trained teacher model, the teacher model including multiple hidden layers; selecting at least two specified layers from the multiple hidden layers of the teacher model and aggregating them to obtain aggregated hidden layers; generating a student model based on the aggregated hidden layers and the remaining unspecified layers in the teacher model; distilling the student model using the teacher model; and reusing the distilled student model as the teacher model until it is impossible to select at least two specified layers from the teacher model, at which point the teacher model at this point is used as the target model. Accordingly, this specification also discloses a distillation apparatus for the fine-tuning stage of a text-based model.

The embodiment of the invention provides a classification model training method and device and a classification detection method and device, and relates to the technical field of artificial intelligence. Inputting the input data into a classification model and a teacher model for classification prediction to obtain a student classification result, a teacher classification result, student intermediate features and teacher intermediate features of each training stage, and for each training stage, calculating a heterogeneous alignment loss value according to the student intermediate features and the teacher intermediate features, calculating a teacher distribution consistency loss value according to the teacher classification result and the teacher intermediate feature, calculating a distribution alignment loss value according to the student classification result and the teacher classification result, and calculating a student task loss value according to the student classification result and the data label corresponding to the input data; and calculating a total loss value based on all the loss values to train a classification model. The heterogeneous semantic gap is reduced by utilizing multi-level feature alignment and dynamic loss optimization, perception and adaptation to the learning track of the student model are realized, and the knowledge distillation accuracy of the heterogeneous model is improved.

Embodiments of the present specification provide a kind of training method of classification model, the method and device of user classification, method includes: the element in the feature representation vector of sample user is randomly set 0 process, obtain missing representation vector;Missing representation vector is input into encoder, obtain the mean vector and variance vector of Gaussian distribution that the hidden vector corresponding to sample user is subject to;Based on mean vector and variance vector, generate first hidden vector, input first hidden vector into decoder, obtain the reconstruction representation vector of sample user;According to the difference between reconstruction representation vector and feature representation vector, determine first loss term;Mean vector is input into classifier, obtain the probability distribution of each preset category to which sample user belongs;According to the category label and probability distribution of sample user, determine second loss term;Total loss function includes first loss term and second loss term.Can improve the performance of classification model when the value of user feature exists missing.

An Artificial Intelligence (AI) based computational system is used to non-invasively estimate the presence of a range of aneuploidies and mosaicism in an image of embryo prior to implantation. Aneuploidies and mosaicism with similar risks of adverse outcomes are grouped and training images are labelled with their group. Separate AI models are trained for each group using the same training dataset and the separate models are then combined, such as by using an Ensemble or Distillation approach to develop a model that can identify a wide range of aneuploidy and mosaicism risks. The AI model for a group is generated by training multiple models including binary models, hierarchical layered models and a multi-class model. In particular the hierarchical layered models are generated by assigning quality labels to images. At each layer the training set is partitioned in the best quality images and other images. The model at that layer is trained on the best quality images, and the other images are passed down to the next layer and the process repeated (so the remaining images are separated into next best quality images and other images). The final model can then be used to non-invasively identify aneuploidy and mosaicism and associated risk of adverse outcomes from an image of an embryo prior to implantation.

The invention belongs to the technical field of open set recognition, and discloses a hyperspectral open set recognition system and method based on class semantic reconstruction. The hyperspectral open set recognition system based on class semantic reconstruction comprises a grouping spectral space reservation transformer module which comprises three stages of hierarchical structures, and each stage of hierarchical structure comprises a grouping pixel embedding module and a space enhancement feature transformation module. The spectrum-spatial feature fusion module is used for fusing the stage spectrum-spatial features into discriminative spectrum-spatial features; the class semantic reconstruction module comprises a plurality of class auto-encoders and is used for performing class semantic reconstruction on the discriminative spectral-spatial features to obtain reconstruction errors, constructing and training a classification model through the reconstruction errors and finally realizing open set recognition in combination with a multi-dimensional scoring function; according to the method, inter-class confusion can be reduced, and interference of background noise information is reduced by reconstructing semantic features instead of original pixels.

Embodiments of the present application provide a text intention classification model training method, a recognition method, a device, an electronic device and a storage medium, belonging to the field of data processing. The training method comprises: obtaining the word group of each training text and the labeled label of each training text, obtaining an initial model comprising a graph neural network and a classifier, taking all labeled labels as the iterative input of the graph neural network to enable the graph neural network to learn the correlation between all intention labels, taking the word group of the training text and the correlation as the iterative input of the classifier, training the initial model, and training a classification model for obtaining the labeled label of the training text, so that the trained classification model can adapt to the application scenario of data distribution change, greatly improving the classification accuracy of the classification model.

The present disclosure relates to a classification method and a training method of a classification model, an apparatus, a device and a medium. The classification method comprises: obtaining a to-be-classified object; obtaining a first category and intermediate features of the to-be-classified object by using a classification branch model in a classification model based on the to-be-classified object; obtaining a second category by using a verification branch model in the classification model based on the first category and the intermediate features; and determining the second category as the category of the to-be-classified object. According to the classification method and the training method of the classification model, the verification branch model is added to the traditional classification model, so that the classification result of the classification branch model is verified again, the precision rate of the classification branch model is further improved, and the classification accuracy is improved.

The invention discloses a class model inspection method, electronic equipment, a storage medium and a product. The method comprises the steps of obtaining context information of a target micro-service application in response to a class model check request from a client; performing class model compliance check based on the context information to generate a corresponding error check result; wherein the class model compliance check comprises at least one of the following items: class member reference validity check, interface and abstract method implementation integrity check, technical suite compatibility check, class name uniqueness check and request path uniqueness check; sending an error checking result to the client for visual display; receiving a correction instruction from the client; the correction instruction is associated with a target error item in the error checking result; and executing a response operation corresponding to the correction instruction. According to the scheme, the multi-dimensional compliance check is executed based on the micro-service context, and the class model design quality and efficiency are improved through visual interaction and intelligent response.

A method for receiving a full training data set including a plurality of individual training data set, dividing the plurality of individual training sets into N classes, where N is an integer greater than three, dividing the N classes into M full data classes and N-M partial data classes, performing training to obtain a trained fixed size machine learning (ML) classification model and a trained in-class confidence model, outputting a first set of prediction value(s) based on the performance of training, distributing each class of the N classes of individual training data sets to a different node of a distributed machine learning system; and outputting, from the nodes of the distributed machine learning system, a second set of prediction value(s) for each class of the N classes.

Provided in the present application are a classification model updating method and apparatus, and a device and a storage medium, which are used for improving the classification precision of a classification model. The method comprises: determining a first new-type sample set from a test sample set on the basis of a first classifier in a first classification model, wherein the first classifier is used for classifying a sample into a first known-type sample which belongs to a recognizable first known type or into a first new-type sample which belongs to an unrecognizable first new type, and one sample comprises the value of at least one statistical feature corresponding to one piece of network traffic data; training a second classifier on the basis of the first new-type sample set, wherein the second classifier is used for outputting the type of a sample, apart from the first new-type sample and the first known-type sample, as an unrecognizable second new type; and adding the second classifier to the first classification model so as to obtain a second classification model, wherein the second classification model is used for classifying a sample into any one of the following types: the first known type, the first new type and the second new type.

A machine learning model having membership estimation resistance may be efficiently generated by executing a process including: inputting a plurality of pieces of processed data, each of which is associated with a ground truth label and each of which is different from basic data, to a first class classification model trained using the basic data associated with the ground truth label to obtain a confidence level of the ground truth label for each of the plurality of pieces of processed data; specifying the processed data that corresponds to the confidence level lower than a first reference value; and training a new class classification model using the specified processed data as training data.