36 results about "Misclassification error" patented technology

The invention discloses a code generation and restoration method and device based on multi-round feedback of a large language model, and the method comprises the following steps: 1, converting problem description into a structured tetrad (target, input, output and constraint) through a two-stage task demand deconstruction mechanism, and generating a pseudo-code frame according to the structured tetrad; 2, generating a plurality of candidate code schemes in parallel based on different implementation strategies; 3, selecting an optimal code implementation scheme through multi-dimensional evaluation; 4, generating an extended test case through boundary condition analysis and public test reasoning, and expanding a verification range; 5, classifying error types in combination with static analysis and dynamic execution information, and applying a special repair strategy; 6, providing detailed repair reasons and ideas by an interpretable repair mechanism; and 7, establishing a multi-round feedback repair verification iteration mechanism composed of testing, analysis, repair and verification. According to the method, the performance of an existing large language model in processing a complex programming task is effectively improved.

The invention discloses a VerilogA code automatic error correction method based on a classification error library and a large language model, and the method comprises the steps: constructing a classification system of the error types of VerilogA codes, starting from an MTJ device, carrying out the statistics of 84 common errors in the VerilogA codes of MTJ based on the system, and developing an automatic training sample generation system according to the 84 common errors; the method comprises the following steps: selecting a DeepSeek-R1-7B model as a basic model through a benchmark test, and carrying out fine tuning training by utilizing a classified error data set generated by scripting to form a special error correction model for the field. The processing process comprises the steps that VerilogA codes to be corrected are received, error types and reasons are positioned through a trained model, correction codes are generated, and verification of eight types of typical circuits is passed. An error classification-model training-simulation verification system is established, verification and error correction of VerilogA codes are achieved, and time and resources in the development process are saved.

The invention discloses a table-to-text generation method based on multi-agent collaboration, which is characterized in that model training comprises two stages of supervised fine tuning and stylized preference optimization, enhanced training data is constructed through table replacement invariance, a large language model is efficiently fine-tuned by using PEFT, and the model is optimized; and a SimPO algorithm is adopted to carry out positive and negative sample contrast learning to generate a text conforming to a golden standard style. In error correction, through a multi-agent cooperation mechanism, table data is coded by using a BERT model and stored in a Faiss vector database, an agent is generated to generate a text based on a large language model, the agent is retrieved to construct a minimum related table, the agent is verified to compare unit declarations and classify errors, and the agent is scheduled to trigger an error correction strategy. And errors such as numerical values, knowledge missing and table conflicts are accurately corrected. The whole process is iteratively generated and verified, it is ensured that the generated text is loyalty to facts, consistent in style and free of illusion, and the quality and accuracy of table-to-text generation are remarkably improved.

The invention discloses a simulation training personalized recommendation analysis method, system and device and a medium, and relates to the technical field of computer platform load balancing, and the method comprises the steps: collecting user behavior data, carrying out the preprocessing, and carrying out the feature marking and grading of a user; quantizing user behavior characteristics by using a distributed machine learning platform, and dynamically adjusting user groups through a clustering algorithm; and generating personalized recommendation by adopting collaborative filtering and reinforcement learning algorithms, and dynamically adjusting task difficulty based on real-time feedback to optimize recommendation weight. According to the method, multi-dimensional behavior characteristics are clustered through a K-means + + clustering algorithm, and dynamic user grouping is realized in combination with a dynamic threshold adjustment mechanism; by designing a recommendation algorithm architecture, not only can the new user be ensured to quickly adapt, but also individuation and dynamic adaptation of the recommended content are realized, and the classification error rate is reduced.

This application relates to the field of model training, and particularly to a method, device, computer device, and storage medium for enhancing training data. The method includes: obtaining a training data set; inputting the training data set into a classification model for pre-training to obtain original images with classification errors and classification data; inputting the original images into a preset residual network to obtain the features of the original images; calculating the mutual information of the features according to the classification data, and screening out false features in the features with an influence degree greater than a preset value according to the mutual information; performing data enhancement on the original images according to the false features to obtain target images; generating an enhanced training data set according to the target images, so as to retrain the classification model based on the enhanced training data set. This application can trace the errors of the model, find features correlated with the prediction results, and enhance the original data set to improve the robustness of the model.

The invention relates to the technical field of image recognition, in particular to an automatic express classification system based on the RFID technology, and the system comprises a tag and image reading module, an error verification and signal decoding module, an execution adjustment and target orientation module, and a performance evaluation and adjustment module. According to the invention, through information reading and error checking mechanisms of the RFID and the camera, the accuracy and efficiency of the express automatic classification system are remarkably improved, after the tag information is read preliminarily, the consistency of the image and the RFID data is checked, classification errors caused by tag damage or information errors are effectively reduced, and when the classification errors are found, the accuracy and efficiency of the express automatic classification system are improved. The system can automatically trigger the re-classification operation, ensures that each parcel is correctly classified, reduces the requirement of manual re-classification, improves the automation level of operation, and can adapt to different operation environments by continuously monitoring the processing speed and the correct rate and optimizing the operation parameters according to the evaluation result; and the stability and reliability of the whole system are improved.

A gesture recognition method based on MPE-YOLOv5, belonging to the field of computer vision. This invention solves the problems of low detection accuracy and high classification error rate for small targets in existing lightweight network models. The main technical solutions adopted by the method of the present invention are: Step 1, collecting gesture images and constructing a training dataset based on the collected gesture images; Step 2, building an MPE-YOLOv5 network and training the MPE-YOLOv5 network using the constructed training dataset; Step 3, using the trained MPE-YOLOv5 network to detect a single frame of video image to be detected, and obtaining a gesture target positioning result and classification result in the image to be detected. The method of the present invention can be applied to gesture recognition.

The application discloses a kind of target detection methods based on small sample, comprising the following steps: S1: image acquisition;S2: image pre-processing;S3: construct small sample target detection model;S4: respectively using base class data set and new class data set to small sample target detection model is trained in two stages;S5: the network model obtained using step S4 is tested.This method reduces the dependence of target detection algorithm on a large number of training data, alleviates the problem of difficult data acquisition and high cost of manually labeled data, alleviates the contradiction between different modules that affect each other and reduce performance, improves the detection performance without increasing the training cost;Reduce the detection result error caused by classification error, improve the classification performance of the model.

The invention relates to a method for realizing financial text business opportunity mining based on a large model. The method comprises the following steps: acquiring basic information of announcement data and news data; carrying out preliminary screening on the announcement type data in combination with the label content according to a regular rule; performing content analysis on the announcement data of which the original text information is in the PDF format to obtain text content; performing label identification on different types of text contents in combination with a business target, and outputting corresponding business opportunity labels; and calling the large model to identify entities mentioned in the text, and extracting the relationship and related business opportunity attribute information according to the context related to the subject. By the adoption of the method and device for achieving financial text business opportunity mining based on the large model, the processor and the computer readable storage medium of the processor, the multi-label processing capacity is enhanced, and the hierarchical label classification error rate is remarkably reduced. The noise robustness is improved, and the anti-interference capability of a resistance sample enhancement model is generated.

A method for establishing a defect detection model and an electronic device. A first classification model is established based on a training sample set, wherein the training sample set includes a plurality of training samples. The training samples are respectively input into the first classification model to obtain respective classification results of the training samples. A plurality of outlier samples with classification errors are obtained from the training samples based on the classification results. Part of the outlier samples with classification errors are deleted from the training samples, and the remaining training samples are used as an optimal sample set. A second classification model is established based on the optimal sample set, so that a defect detection is performed by the second classification model.

This application relates to the field of artificial intelligence, providing a model training method, image classification method, and apparatus for embodied intelligent perception. The method includes: constructing a hierarchical input sequence containing global category labels, local category labels, and patch labels; inputting the hierarchical input sequence into a visual Transformer backbone network; combining local mask multi-head attention and Hungarian matching cross-image voting to determine foreground and background local category labels; performing region semantic self-supervised pre-training, triplet conditional feature interaction, and label-level feature enhancement on the visual Transformer backbone network to obtain an image classification model. This application can extract discriminative local region features of targets under small sample conditions and enhance semantic consistency between similar samples, completing the training of the image classification model, thereby reducing background interference and image classification error rate in complex scenes and improving classification accuracy.

The application discloses a table-to-text generation method based on multi-agent cooperation, wherein model training includes two stages of supervised fine-tuning and style preference optimization, enhanced training data is constructed through table replacement invariance, PEFT is used for efficiently fine-tuning a large language model, and SimPO algorithm is adopted to generate text conforming to the golden standard style through positive and negative sample comparison learning. Error correction is performed through a multi-agent cooperation mechanism, BERT model is used to encode table data and store in a Faiss vector database, an agent generates text based on a large language model, a retrieval agent constructs a minimum concern table, a verification agent compares unit statements and classifies errors, a scheduling agent triggers an error correction strategy, and numerical values, knowledge loss and table conflicts are accurately corrected. The whole process is iteratively generated and verified to ensure that the generated text is faithful to the facts, the style is consistent and there is no illusion, and the quality and accuracy of table-to-text generation are significantly improved.

A model management system performs error analysis on results predicted by a machine learning model. The model management system identifies an incorrectly classified image outputted from a machine learning model and identifies using the Neural Template Matching (NTM) algorithm, an additional image correlated to the selected image. The system outputs correlated images based on a given image and a selection by a user through a user interface of a region of interest (ROI) of the given image. The region is defined by a bounding polygon input and the correlated images include features correlated to the features within the ROI. The system prompts a task associated with the additional image. The system receives a response that includes an indication that the additional image is incorrectly labeled and including a replacement label and instruct that the machine learning model be retrained using an updated training dataset that includes the replacement label.

The application discloses an image recognition method based on deep reinforcement learning and an evolutionary algorithm, and comprises the following steps: S1, collecting image data and performing standard processing on the image; S2, defining an optimization target and an optimization target function; S3, solving a multi-target optimization function F based on deep learning and the evolutionary algorithm; S4, performing optimization training on the model based on the deep learning and the evolutionary algorithm; and S5, classifying image data by using the optimized image recognition model. The application can be applied to image recognition, and the model structure is optimized, so that the image recognition precision is effectively improved, and the image classification error rate is reduced.

The present invention claims protection for a weight Bayesian classifier and an ontology mapping method for semantic ontology, which comprises the following steps: S1: Parsing the ontology: Parsing the ontology participating in the ontology mapping through the Jena package in Java, extracting concepts, and using the WordNet semantic dictionary to complete the expansion of concepts; S2: Determining weights using the harmony search algorithm: Introducing a dynamic weight mechanism to reduce the impact brought by its assumption of conditional independence of attributes; S3: Constructing a weight Bayesian classifier: Establishing a weight Bayesian classifier through the weights obtained in the previous step, converting the ontology mapping problem into a classification problem, calculating the maximum posterior probability to complete the classification; finally, saving the output classification result into the AllergoGraph database. The present invention adopts the harmony search algorithm, uses the classification error rate as the objective function, calculates the global optimal weight and assigns it to the weighted naive Bayesian classifier; the present invention proposes a new way of generating solutions to improve the optimization ability of the harmony search algorithm.

A system and method for error detection in dental imaging. The method for detecting errors in a dental panoramic image, comprising converting a dental panoramic image into a byte array. Identifying and extracting critical features from the byte array. Generating a plurality of feature map layers. Generating region proposals for potential errors within each feature map layer. Extracting fixed sized features from the region proposals. Classifying features within each region proposal. Regressing bounding boxes around classified error. Outputting the image with bounding boxes indicating errors within the image, and outputting an indication whether image is viable based on the errors.

The application provides a prototype network feature reconstruction and classification method, device and equipment, comprising: obtaining N prototypes of a prototype network; determining the Euclidean distance between the feature vectors of the i-th prototype in any two prototypes is less than a preset threshold; determining the to-be-inserted feature vector in the prototype according to K feature vectors in the two prototypes; inserting the to-be-inserted feature vector into the set position of the two prototypes respectively, reconstructing the feature of the to-be-identified object through the above method, performing three spline interpolations on the two prototypes after feature reconstruction and the feature of the to-be-identified object after reconstruction, and deriving the two prototypes and the feature of the to-be-identified object after three spline interpolations, and determining the category of the to-be-identified object according to the similarity between the derivative of the feature of the to-be-identified object and the derivative of the two prototypes. The problem that the prototypes in the prototype network are too far away from the target and cause classification errors is solved, and the classification accuracy is improved.

The application discloses a cross-domain long-tail image classification method based on self-supervised learning and self-training mechanism, comprising the following steps: firstly, acquiring a dataset of cross-domain long-tail images, and dividing the dataset into a training set and a test set; then, training a double-branch network model by using the cross-domain long-tail image data in the training set; for the unsupervised adaptive branch, using a self-supervised learning and global distribution alignment method for representation learning; for the supervised adaptive branch, self-training and mutual information maximization are performed on the classifier; using a dynamic weighted ensemble training strategy to automatically adjust the learning weights of the two branches, so as to realize the simultaneous optimization of the learning objectives of the two branches; finally, classifying the cross-domain long-tail image data in the test set by using the trained model. The application solves the problem that the existing classification method is prone to cross-domain long-tail image classification errors due to insufficient extraction of image discriminative features, model decision boundary deviation, error accumulation in the self-training process and the like.

A system (100) for analyzing log data for operational and security anomalies is provided. A preparation module (120) prepares log data by determining the necessary volume, evaluating suitability, and categorizing it. A pattern discovery module (125) identifies structures in the log data using a plurality of methods. A patterned-message structure module ( 130) creates a structured format for each log message and computes hashes for data integrity. A processing module (135) stores and validates the structured logs. A message pattern curation module (140) customizes patterned messages and offers suggestions. A log message grouping module (145) organizes log messages into groups based on patterns and binding variables. An error identification module (150) detects and categorizes errors, highlighting operational and security issues. Finally, a log storage module (155) stores configurations, static log data, and variable content efficiently.

The present application discloses an intelligent classification control system and method for urban garbage recycling, which belongs to the field of garbage recycling. The present application assigns weights to misclassified garbage based on the error analysis results of the misclassified garbage, performs classification effect analysis based on the weight assignment results of the misclassified garbage, classification data of the garbage classification, and classification accuracy data, and issues classification anomaly warnings based on the obtained classification effect analysis results. The present application accurately identifies classification anomalies by performing comprehensive data analysis on classification errors in the garbage classification process, avoids the influence of image recognition factors on the accuracy of judging classification anomalies, and improves the recognition accuracy and efficiency of classification anomalies.

The invention relates to the technical field of agricultural intellectualization, and discloses a method and a device for accurately calculating an operation area based on classification-clustering bidirectional feedback, and the method comprises the steps: determining target trajectory data generated in an operation process of an agricultural machine; based on the target trajectory data, performing operation state classification processing by using a target classification model to determine a target operation data set of the agricultural machine; wherein the target classification model is determined by performing iterative training on the initial classification model by using suspicious trajectory data in the target trajectory data; and performing area calculation based on the effective land parcel units in the target operation data set to obtain a target operation area of the agricultural machine. The target classification model can continuously learn and correct classification errors by performing iterative training on the initial classification model, so that compared with a single classification model, misjudgment and missed judgment can be effectively reduced, the precision of a final operation area calculation result is remarkably improved, and high-standard application requirements can be met.

The application discloses a kind of adaptive demodulation methods of digital oscilloscope, first train GRU neural network in GPU offline and generate coefficient matrix, then coefficient matrix is loaded to the FPGA that deployed GRU neural network, then through the acquisition system of digital oscilloscope, the signal to be demodulated is collected, the sampling data is multiplied with coefficient matrix, and then the modulation classification corresponding to the signal to be demodulated is determined;Finally, according to the modulation classification result of the signal to be demodulated, the corresponding modulation mode is selected, and the adaptive demodulation of the signal to be demodulated is completed, if classification error phenomenon occurs in the demodulation process, then GPU online incremental learning is carried out, so as to provide the accuracy of demodulation.

This invention discloses a targeted adversarial attack method against YOLOv8, belonging to the field of adversarial attack technology. The invention includes the following steps: analyzing the architecture, loss function, and input / output data information of the YOLOv8 object detection model to clarify the output result of the original clean image after model prediction; based on the model architecture, loss function, and input / output data information, constructing three targeted attack targets: object disappearance attack, redundant object attack, and misclassification attack, and designing a corresponding attack loss function for each target; by minimizing the attack loss function, iteratively updating the input image to achieve the three targeted attacks on the YOLOv8 model, generating adversarial sample images in batches; and inputting the adversarial sample images into the YOLOv8 model to obtain the detection result image after the attack. This invention can achieve the three targeted attack results of object disappearance, redundant objects, and misclassification.

The invention relates to the technical field of credit granting data processing, and discloses a centralized approval method for credit granting services, which comprises the following steps: cleaning and classifying application data, checking whether the cleaned data is complete, judging whether the cleaned data is classified correctly, dynamically allocating approval tasks, forming an approval feedback and whole-course tracing system, optimizing an approval model, and carrying out centralized approval. According to the centralized approval method for the credit granting service, whether data omission or classification errors exist in the data preprocessing process or not is automatically judged, and under the condition that the data are inaccurate or incomplete, the data are actively recovered and adjusted; the condition that a client submits data again or an examination and approval result is inaccurate due to inaccurate or incomplete data is avoided, the examination and approval task is dynamically allocated for the application data according to the specific condition, examination and approval feedback and whole-course tracing are timely formed, the examination and approval model is timely optimized, the examination and approval process is simplified, the examination and approval time is shortened, and the examination and approval efficiency is improved. And the accuracy and objectivity of the approval result are improved.

The invention discloses a near-infrared nonlinear discrimination method for fruit sorting, and relates to the technical field of artificial intelligence. The training process of a classification discrimination model is as follows: obtaining a spectral data set of fruits and preprocessing it, performing moving smoothing processing on the preprocessed spectral data set, and mixing the preprocessed spectral data set with the spectral data set after moving smoothing processing as a sample data set; randomly sampling the sample data set by using a Monte Carlo method to divide a training set and a first test set, training a discrimination model based on the training set, obtaining a classification result of the random sampling, and counting the classification error rate of each sample; constructing a sample weight coefficient based on a probability density value corresponding to a chemical value of each sample and the classification error rate of each sample; constructing a nonlinear discrimination model by using the sample weight coefficient, and optimizing the nonlinear discrimination model; the near-infrared nonlinear discrimination method improves the accuracy and reliability of fruit sorting.

The invention provides an intelligent goods sorting and tracing method and system, and the method comprises the steps: S5, carrying out goods distribution operation through employing automatic sorting equipment according to a final classification result, recording a sorting path and a timestamp through a real-time monitoring system, and obtaining a complete data chain of a sorting process; step S7, acquiring a hash value in the data chain, performing association matching with a goods classification result through a query interface, judging whether the tracing request is consistent with an original record, and obtaining a credible tracing verification result; s8, extracting abnormal data from a tracing verification result, analyzing specific links of classification errors or data tampering by adopting an anomaly detection algorithm, and determining a source position of a problem; and S9, aiming at the source position of the problem, adjusting a weight parameter of a multi-modal fusion algorithm through a feedback mechanism, obtaining an optimized goods description vector, and judging the performance improvement of the sorting system.

This invention discloses a few-shot segmentation method, apparatus, terminal, and medium based on an attention mechanism, comprising: inputting a support image, a mask image corresponding to the support image, and a query image to be predicted; extracting features from the query image to be predicted and features from the support image using a weight-shared encoder; inputting the features from the support image and the mask image corresponding to the support image into a prototype generation algorithm to obtain a pair of complementary prototypes; expanding the pair of complementary prototypes to the size of the query image to be predicted and concatenating them with the query image to be predicted; predicting the foreground and background of the concatenated image using an FPN-structured decoder to obtain the segmentation result; and restoring the segmentation result to the original image size using a bilinear interpolation algorithm to obtain the few-shot segmentation result. This invention constructs a pair of prototypes in a weighted complementary manner, thereby preserving as much effective information as possible during prototype generation, reducing classification errors caused by prototype bias, and improving judgment accuracy.

The invention discloses a method and a device for determining the category of demand information. The method comprises the following steps: when target demand information is received, performing intelligent classification processing on the target demand information through a decision model to obtain a first demand category of the target demand information; analyzing the target demand information to obtain a second demand category of the target demand information; verifying the first demand category according to the second demand category to obtain a verification result; when the verification result shows that the first demand category is consistent with the second demand category, determining that the first demand category is the demand category of the target demand information; and when the verification result shows that the first demand category is inconsistent with the second demand category, correcting the first demand category, and taking a correction result as the demand category of the target demand information. According to the invention, the technical problems of uncertainty caused by human factors and easy occurrence of classification errors during demand management in the prior art are solved.

The present application belongs to the field of electric digital data processing, and particularly relates to a zero-shot named entity recognition method and device based on large model feedback optimization. The method iterates an entity extraction strategy to force the large model to focus on the remaining part of the text to be extracted except the known entity, thereby improving the recall rate of entity recognition. In order to balance the recall rate and the precision rate, the method further proposes an entity filtering strategy based on the large model to realize the screening of candidate entities. After screening, the large model is guided to focus on the entity type classification task, effectively correcting the classification errors that may be generated in the extraction process. At the same time, a text-based entity filtering strategy is introduced in the entity extraction process to suppress the invalid or error entities caused by the large model illusion problem, thereby significantly improving the accuracy and stability of the overall recognition.