15 results about "Multi label learning" patented technology

The invention relates to the technical field of artificial intelligence, in particular to a business contract intelligent review and risk quantification method and device, and the method comprises the steps: building and maintaining a business contract key term information base; obtaining and preprocessing a to-be-rechecked contract text; processing the text based on a bidirectional long-short term memory network and a conditional random field model, and extracting semantic representation; identifying key information through a multi-level attention mechanism; executing multi-label learning to classify and identify clause types and attributes; utilizing a dependency syntactic analysis technology to extract logical association and a responsibility chain among terms, and constructing a knowledge graph; identifying risk terms and generating risk prompts; business indexes are extracted, and risk open values are calculated; generating a rechecking report; the corresponding device comprises nine functional modules such as an information base management module, a text preprocessing module and a semantic representation extraction module, risk terms in a contract can be automatically recognized, a quantitative risk assessment result is provided, and contract auditing efficiency and accuracy are effectively improved.

The invention discloses a dual-channel label-guided multi-label feature selection method and system, and belongs to a feature engineering technology. The method mainly comprises the steps of obtaining a feature matrix and a positive label matrix of a multi-label data set, performing logic negation on the positive label matrix to generate a mirror image negative label matrix, and constructing a graph Laplacian matrix based on the feature matrix; constructing a multi-label model based on the preprocessed data, wherein an objective function of the multi-label model at least comprises a positive label regression loss item, a negative label regression loss item, a label alignment constraint item, a graph regularization item and a sparse constraint item; constructing an optimization function through relaxation processing constraint and in combination with a Lagrangian multiplier method, iteratively solving the objective function according to a KKT condition, and evaluating feature importance based on a projection matrix for associating features and positive tags after iterative convergence; according to the method, the requirement of multi-label learning for accurate and efficient feature screening is met, label information can be comprehensively utilized, the anti-interference capability is enhanced, and the efficiency is considered.

The application discloses a kind of double-channel label guide multi-label feature selection method and system, belong to feature engineering technique.Method mainly includes: obtaining the feature matrix and positive label matrix of multi-label data set, by performing logical negation to positive label matrix, generate mirror negative label matrix, and construct graph Laplacian matrix based on feature matrix;Based on the data after pre-processing, a multi-label model is constructed, and the objective function of the multi-label model includes at least positive label regression loss term, negative label regression loss term, label alignment constraint term, graph regularization term and sparse constraint term;The constraint is processed by relaxation, and the optimization function is constructed by combining the Lagrange multiplier method, and then the objective function is iteratively solved according to the KKT condition, and after iterative convergence, the feature importance is evaluated based on the projection matrix used to associate features and positive labels;The application meets the demand of multi-label learning for accurate and efficient feature selection, can fully utilize label information, enhance anti-interference ability and consider efficiency.

The invention relates to an incomplete multi-view multi-label classification method based on cross-view distillation and adaptive mask. The method comprises the following steps: acquiring incomplete multi-view multi-label data; and inputting the incomplete multi-view multi-label data into a multi-label classification model, extracting multi-view depth representation, performing mask filtering and fusion on low-quality representation, and inputting the fused representation into a multi-label classifier to obtain a corresponding prediction result, the multi-label classification model is obtained through training of incomplete multi-view multi-label training data, and multi-view distillation and a self-adaptive shielding method are fused in the training process. According to the method, multi-view distillation and adaptive shielding technologies are fused, and the inherent unbalance and noise problems in an incomplete multi-view multi-label learning task are solved.

The invention discloses a video second-level label labeling method based on deep part multi-label learning, relates to the technical field of computer vision and deep learning, and solves the problems of high labeling cost and label ambiguity due to the fact that traditional video label labeling depends on manual labeling or a fully supervised learning model. According to the technical scheme, the method is characterized by comprising the following steps of dynamic graph construction and instance graph construction, wherein a sparse instance affinity matrix is calculated through a monomial kernel; the method has the advantages that only video-level weak labels are needed to generate the candidate label set, manual labeling of accurate labels per second is not needed, and labeling workload and labeling cost are reduced; false positive tags in candidate tags are effectively filtered through a graph deblurring technology based on a partial multi-tag identification technology of a depth model, and the accuracy of second-level tags is improved; the method is suitable for various video scenes (such as sports events and daily activities), and can be expanded to more tag categories.

The invention discloses a plate strip steel surface defect detection method based on partial multi-label learning, and belongs to the technical field of industrial quality detection and data mining. In order to solve the problem that in the prior art, due to the fact that labeling noise is difficult to eliminate, model robustness is insufficient, a semantic alignment mechanism is introduced, and collaborative modeling is carried out on a sample feature space and a label semantic space. The process comprises the following steps: in a data preparation stage, extracting plate strip steel image features and constructing candidate tags; in the label denoising stage, a partial multi-label learning framework is adopted, label false correlation is eliminated through orthogonal rotation, label reliability is improved through joint projection, a label relation is reconstructed through manifold learning, and finally a denoised discrimination label is obtained. In the classifier training stage, a depth perception classifier is trained by using a discriminant label and an original label; and in the detection stage, defects such as cracks and scratches are identified. According to the method, the detection precision and the anti-interference capability are improved, the dependence on manual labeling is reduced, and reliable support is provided for plate and strip steel quality control.

The application provides a small sample incremental fault diagnosis method and system combining contrast learning and multi-label learning, a storage medium and an electronic device, and relates to the technical field of intelligent manufacturing and equipment health management.In the application, a small sample set containing all fine-grained fault categories is constructed;based on the source domain data of the small sample set, a model combining supervised contrast learning and multi-attribute learning is pre-trained;the feature extractor of the pre-trained model is frozen again, and based on the target domain data of the small sample set, the linear classification head of the pre-trained model is fine-tuned, and finally the prediction of fine-grained fault categories is realized.The application can be applied to fine-grained fault identification and new fault identification of various mechanical equipment such as rotating machinery and gearboxes in different working conditions, and can effectively locate the working condition, severity and specific type of fault occurrence, and accurately trace the real cause and detailed logic of fault occurrence.

The invention discloses a BERT and naive Bayes integration fused multi-label sensitive text detection method, and belongs to the technical field of data security detection, and the method comprises the steps: extracting deep semantic features from an input text through a pre-trained BERT model, understanding a subtle language pattern of sensitive content, and calculating the prediction probability of the BERT model; constructing a naive Bayes integration model of multi-label learning, and calculating a prediction probability of the naive Bayes integration model based on a lexical statistical law; consistency regularization loss is introduced in the training stage, and the prediction probability of a BERT model and a naive Bayes integration model is forcibly required to align each label; in the reasoning stage, the two models are jointly deployed, fusion prediction is carried out in a probability mode, and a multi-label sensitive text detection result is obtained. According to the method, the robustness to adversarial attacks such as code switching can be enhanced, and the sensitive text detection precision in a multi-language scene is effectively improved.

The application discloses a kind of unmanned plane power station inspection fault detection methods based on improved YOLOv12 and multimodal deep feature fusion, it is first by to visible light image, infrared thermal imaging image and laser radar point cloud image are carried out image enhancement, pseudo color mapping etc. Preprocessing operation, improve the quality of input data and the intermodal fusibility;Second, construct spatial alignment and channel stacking mechanism, and introduce MMFM module in backbone feature extraction module, realize the saliency guidance and dynamic fusion of multi-modal feature, to enhance the detection robustness of model in low illumination, shielding and weak thermal signal Complex environment;Third, integrate MADSE module in backbone feature extraction module, construct multi-scale dynamic perception structure, effectively improve the synchronous identification ability to micro-fault and macro structure;While combining MGFP module, fine modeling the complementary relationship and interference suppression ability between modes, improve the expression effect of fusion feature;Finally, construct multi-label learning framework.

The application discloses the technical field of coding strategy and relates to a multi-instance multi-label learning method based on a combined error correction coding strategy. The multi-instance multi-label learning method based on the combined error correction coding strategy comprises the following steps: a feature embedding representation method of multi-instance bag level data structure information is designed based on a Fisher kernel, and an original multi-instance multi-label learning problem is converted into a multi-label learning problem; under the guidance of an error correction output coding idea, a coding strategy of randomly connecting multi-label sub-problems is proposed; a support vector machine model is constructed and trained, and hard labels representing the specific category performance of a predicted sample under each label are obtained based on a T criterion. The multi-instance multi-label learning method based on the combined error correction coding strategy effectively alleviates the class imbalance problem. In addition, the model divides different training data blocks based on coding random division to learn base classifiers, so that the similarity between the base classifiers is weakened, and the classification coding error correction capability is stronger.

The present application relates to the technical field of information security, and in particular to a multi-label learning based side-channel analysis method for different devices, which first proposes a shortest principle to form the energy traces corresponding to the single-byte key labels of each device according to the characteristics of the energy trace data sets collected under different devices and the requirements for deep learning training, and forms a multi-label data set for different devices, then applies the multi-label learning technology to the side-channel environment, realizes the algorithm adaptation method of multi-label learning based on a convolutional neural network, further sets various hyperparameter combinations to optimize the multi-label learning model, and finally uses the corresponding test sets under each device to evaluate the byte key attack effect of the multi-label model on the cryptographic algorithm under different devices, so as to verify the generalization ability of the multi-label learning model. The present application enables a single model to perform attacks on the same cryptographic algorithm under different devices, increases the attack efficiency, and reduces the model construction and training time.

An image classification method based on a weakly supervised autoencoder, specifically: classifying unlabeled images and... k A labeled reference image is input into the input layer of the autoencoder model; the mean squared error between the reconstructed output value and the original input value of the original autoencoder model is calculated as the mean squared error term; the sum of squared weights between the input layer and the hidden layer is calculated as the weight decay term; the energy function of the entire autoencoder model is calculated as the energy regularization term; the soft multi-label consistency similarity metric for discriminative vision is calculated as the soft multi-label learning term; the loss function of the autoencoder model is minimized using gradient descent within a specified number of iterations to obtain the optimal parameter weights and biases of the autoencoder model, and the autoencoder model is pre-trained; test data is input into the autoencoder model that has been adjusted to the optimal value, and then the features extracted from the hidden layer are input into the classifier for classification to obtain the classification result.

This invention discloses a method for learning and classifying incomplete multi-view, incomplete multi-label data based on view information quality estimation. The method includes: acquiring several incomplete multi-view, incomplete multi-label data sets; constructing an incomplete multi-view, incomplete multi-label learning model; inputting the incomplete multi-view, incomplete multi-label data into the incomplete multi-view, incomplete multi-label learning model for training; and acquiring test complete or incomplete multi-view data; inputting the test complete or incomplete multi-view data into the trained incomplete multi-view, incomplete multi-label learning model to complete the multi-label classification of the multi-view data. This invention accurately predicts the complete label of each input sample based on the partial or complete multi-view features of each input sample.

The application relates to a multi-label learning method, device and equipment based on sample missing label enhancement, which comprises the following steps: acquiring a training data set of a missing label sample; pre-processing the training data set to obtain a processed training set with restored real labels; learning and aggregating the processed training set by using an algorithm adaptation strategy to obtain a multi-label learning classifier; taking the classifier as a label prediction model; and inputting a sample to be predicted into the label prediction model to obtain labels corresponding to the sample to be predicted. The method realizes label information enhancement by obtaining the processed training set with restored real labels; then the processed training set is induced by using the algorithm adaptation strategy to obtain a classifier considering the class imbalance problem in the processed training set; and the label prediction model is constructed based on the classifier to solve the multi-label class imbalance problem and improve the precision and accuracy of the predicted labels.

The invention discloses an unmanned aerial vehicle power station inspection fault detection method based on improved YOLOv12 and multi-modal depth feature fusion, and the method comprises the steps: firstly carrying out the preprocessing operation of image enhancement, pseudo-color mapping and the like on a visible light image, an infrared thermal imaging image and a laser radar point cloud image, and improving the quality of input data and the fusion between modals; secondly, constructing a space alignment and channel stacking mechanism, and introducing an MMFM module into a trunk feature extraction module to realize significance guidance and dynamic fusion of multi-modal features, thereby enhancing the detection robustness of the model in complex environments of low illumination, shielding, weak thermal signals and the like; thirdly, an MADSE module is integrated in a trunk feature extraction module, a multi-scale dynamic sensing structure is constructed, and the synchronous recognition capability of tiny faults and macrostructures is effectively improved; meanwhile, in combination with an MGFP module, the complementary relationship between modeling modes and the interference suppression capability are finely modeled, and the fusion feature expression effect is improved; and finally, constructing a multi-label learning framework.