41 results about "Class discrimination" patented technology

The invention, which belongs to the field of voice signal processing and machine learning, relates to a deep-belief-network-characteristic-vector-based channel-robust voiceprint recognition system comprising a voice acquisition and preprocessing module, an original spectral characteristic extraction module, a deep belief network training module, a speaker voiceprint characteristic vector extraction module, a speaker acoustic model generation module and a speaker identification module. On the basis of voice data from different channels and corresponding speaker identity numbers, a deep belief network is trained in a manner of supervision; and a discrimination ratio is provided to select a deep belief network hidden layer output having an optimal class discrimination property, thereby constructing a speaker voiceprint characteristic vector having channel robustness. Compared with the traditional i-vector-based speaker confirmation system, the provided system has higher voiceprint recognition accuracy on the condition of channel mismatching.

The invention discloses a brain image segmentation method and system based on a saliency learning convolution nerve network; the brain image segmentation method comprises the following steps: firstly proposing a saliency learning method to obtain a MR image saliency map; carrying out saliency enhanced transformation according to the saliency map, thus obtaining a saliency enhanced image; splitting the saliency enhanced image into a plurality of image blocks, and training a convolution nerve network so as to serve as the final segmentation model. The saliency learning model can form the saliency map, and said class information is obtained according to a target space position, and has no relation with image gray scale information; the saliency information can obviously enhance the target saliency, thus improving the target class and background class discrimination, and providing certain robustness for gray scale inhomogeneity. The convolution nerve network trained by the saliency enhanced images can be employed to learn the saliency enhanced image discrimination information, thus more effectively solving the gray scale inhomogeneity problems in the brain MR image.

An inspection apparatus includes a discrimination function determination unit which determines whether or not a discrimination function forms an area including a discrimination sample. The discrimination function is used in non-parametric one-class discrimination. The discrimination sample is discriminated into a class as a single area in an input space where learning samples are plotted.

The invention discloses a cross-domain text sentiment classification method based on domain countermeasure self-adaption. The method comprises the following steps: inputting a word vector matrix, a category label and a domain label of a source domain sample and a target domain sample; Utilizing a feature extraction module based on a convolutional neural network to extract low-level features of thesample; constructing a constraint based on distribution consistency of a source domain and a target domain in a main task module, mapping a low-layer sample to a regeneration kernel Hilbert space, and learning a high-layer feature with transferability; inputting the high-level features of the source domain into a class classifier, and ensuring that the classifier has class discrimination on samples on the basis of reducing domain difference; a domain invariance constraint based on adversarial learning is constructed in an auxiliary task module, and low-level features are input into a domain classifier with adversarial properties, so that the classifier cannot judge the domain to which a sample belongs as much as possible, high-level features with domain invariance are extracted, and the migration problem of a source domain classifier to a target domain is effectively solved.

The invention relates to an action recognition method based on a neural network and an action recognition device based on the neural network. The method comprises the steps that a video to be recognized is inputted to a trained first three-dimensional neural network model to be processed so that the action extraction result of the video to be recognized is obtained; the action instance detection result of the video to be recognized is determined according to the action extraction result of the video to be recognized; the video to be recognized is inputted to a trained second three-dimensionalneural network model to be processed so that the action class discrimination result of the video to be recognized is obtained; and the action class of the video to be recognized is determined according to the action instance detection result of the video to be recognized and the action class discrimination result of the video to be recognized. Different recognition results obtained by using two three-dimensional neural network models are combined so that the recognition efficiency of the three-dimensional neural network models can be enhanced and the computational burden of the single three-dimensional neural network model can be reduced.

Classifiers, which are combinations of a plurality of weak classifiers, for discriminating objects included in detection target images by employing features extracted from the detection target images to perform multi class discrimination including a plurality of classes regarding the objects are generated. When the classifiers are generated, branching positions and branching structures of the weak classifiers of the plurality of classes are determined, according to the learning results of the weak classifiers in each of the plurality of classes.

The invention discloses a tissue pathology image recognition method. The method comprises the following steps of choosing disease-free and diseased training samples and disease-free and diseased testing samples; combining the disease-free training sample and the diseased training sample, establishing a disease-free dictionary study model and a diseased dictionary study model, alternately iterating and optimizing two target functions till reaching maximum iteration frequency, and obtaining a disease-free dictionary and a diseased dictionary through study; utilizing the disease-free dictionary and the diseased dictionary, conducting sparse representation on the testing samples, and calculating a sparse reconstruction error vector of the testing samples under the disease-free dictionary and the diseased dictionary; obtaining a classification statistic through the sparse reconstruction error vector, and comparing the classification statistic with a threshold value to obtain the classification of the testing samples. According to the tissue pathology image recognition method, a new model and method is provided for dictionary study in a tissue pathology image classification, and the studied dictionary with type class has good sparse reconfigurability and intra-class robustness for similar samples, and has good class inter-class discrimination for non-similar samples.

A hyperspectral image migration classification based on a depth joint distribution adaptive network includes such steps as inputting hyperspectral images in source domain and target domain, normalizing features and unifying dimensions; combining features of hyperspectral images in source domain and target domain; The edge probability distribution adaptation network is constructed to adapt the edgeprobability distribution of hyperspectral images in source domain and target domain. According to the principle of one-to-many classification, the training samples of hyperspectral images in source domain and target domain are selected. A conditional probability distribution adaptation network is constructed to adapt the conditional probability distribution of the hyperspectral images in the source domain and the target domain. One-to-many classification of hyperspectral images in target domain is performed. A depth-based joint distribution adaptation network is proposed, which realizes feature adaptation of a source domain and a target domain hyperspectral image, and reduces that joint probability distribution difference between the source domain and the target domain. At the same time,one-to-many classification model is used to improve the intra-class and inter-class discrimination, and then the accuracy of hyperspectral image migration classification is improved.

The invention relates to a feature selection method and application based on feature identification degree and independence. The method comprises following steps: calculating the importance degree of each feature by measuring inter-class distinguished ability with feature identification degree and measuring correlational relationship between features with feature independence and sequencing in a descending order; and selecting top k features with the importance higher than those of others to form a feature subset with high class-discrimination performance. Differently-expressed gene subsets selected in application of oncogene expression profile data obtain fine time and class discrimination performance. The feature selection method and application based on feature identification degree and independence have following beneficial effects: easy calculations can be made; time complexity is reduced; selection efficiency runs high; and a good reference is provided for clinical diagnoses and judgments of tumors and other diseases.

The invention particularly relates to an IG TF-IDF text feature vector generation and text classification method, and belongs to the field of text mining and machine learning. The method comprises thefollowing steps: 1) generating a text feature vector; 2) train that classifier; 3) evaluate that classification performance; 4) classify that target text set; The weight calculated by the invention can more truly reflect the importance of different terms to the text classification, so that the term with strong class discrimination ability is allocated with larger weight, the weight calculation ismore reasonable, and the accuracy of the text classification is improved. Moreover, the calculated term weights do not need to know the specific categories, thus overcoming the shortcomings of supervised methods such as TFADF in multi-category text classification.

The invention provides an inspection apparatus and an inspection method. The object of the invention is to determine criterion function and in order to make the criterion function used for nonparametric 1-class discrimination form a single on-spec product region. In a discriminant function deciding section 20 in an inspection device, nondefective region discriminating section 26 determines, in an input space in which the discriminant function used for the nonparametric 1-class discrimination plots a sample, whether a region containing the sample discriminated into the class is formed as a single region. Then, when the region containing the sample discriminated into the class is not single, a parameter setting section 24 sets region parameters for defining the size of the region of the basis function of the base of a density function so that the discriminant function constitutes a single region in an input space in which the sample is plotted. Wherein, the region parameters prescribes criterion function and the region size of basis function as basic of criterion function.

The invention discloses an early warning method for giving precedence to pedestrians under new traffic rules and an early warning system for giving precedence to the pedestrians under the new trafficrules. The method comprises the steps that vehicle driving information, pedestrian information and traffic sign line information are acquired; the pedestrian information is detected by using a pedestrian detection algorithm based on deep learning so as to complete pedestrian position detection and class discrimination; the traffic sign line information is detected by using a traffic sign line detection algorithm, and the traffic sign line is extracted; and whether the pedestrians have the intention of crossing the road is judged according to the result, the relative position of the pedestriansand the vehicle is analyzed if the judgment result is yes, and an early warning signal is emitted to prompt the vehicle driver to stop to wait for the pedestrians to pass when the vehicle meets the stop rules of giving precedence to the pedestrians. The system comprises a sensor, an embedded platform and an early warning device and is used for performing early warning in case of giving precedenceto the pedestrians under the new traffic rules. According to the early warning method for giving precedence to the pedestrians under the new traffic rules and the early warning system for giving precedence to the pedestrians under the new traffic rules, the driver can be assisted to be clear about the rules of giving precedence to the pedestrians, and the safety of the pedestrians can be guaranteed.

The invention relates to the technical field of computers and provides a text classification method and device. The method comprises the steps of S1, determining a feature word set of each target textby utilizing a key word bank expansion-based feature selection rule; S2, by utilizing an intra-feature word class uniformity and inter-feature word class discrimination degree-based weight calculation formula, calculating a weight of each feature word in the feature word set; S3, by utilizing a maximum weight fusion algorithm, performing weight fusion calculation on the weights of the feature words of the same target text in different text categories to construct target text eigenvectors; and S4, based on the target text eigenvectors, classifying target texts by utilizing a multi-mark classification model. According to the text classification method and device provided by the invention, the accuracy of text information expression can be effectively improved; the efficiency of model building can be improved; and the text information is ensured to be subjected to multi-mark classification accurately and efficiently.

The invention discloses a knowledge graph-based generative zero sample prediction method, which comprises the following steps: constructing a knowledge graph fusing various semantic information by taking a hierarchically structured category as a category node and taking category connection attribute description, text description and external knowledge as additional nodes; encoding semantic information of the knowledge graph by adopting a graph neural network algorithm to generate category vector representation; and using the generated category vector representation as an input to a generationmodel to generate a sample of the category for learning and prediction of a zero sample learning algorithm. According to the method, the knowledge graph fusing various semantic information is constructed, and the samples with richer features and higher inter-class discrimination are generated for each invisible class based on the knowledge graph, so that the prediction problem of the invisible class samples is well solved.

A learning sample collector is configured to collect learning samples which have been classified into respective classes through N-classes discrimination (N is a natural number of 3 or more). A classifier is configured to reclassify the learning samples collected by the learning sample collector into classes applied to M-classes discrimination, where M is smaller than N (M is a natural number of 2 or more and is less than N). A learner is configured to learn a discriminator for performing the M-classes discrimination on a basis of the learning samples reclassified by the classifier.

The invention discloses an image retrieval method based on sketches. The image retrieval method comprises the following steps of S1, tranining classification models of two CNNs corresponding to the sketches and photos respectively; S2, constructing a retrieval model by using the classification model obtained in the step S1, and training the retrieval model based on quadruplet loss; preprocessing the images in the image library; retrieving a single model; fusing results obtained by the plurality of retrieval models to obtain a final retrieval result. The method is based on the theory that the feature vector spacing corresponding to the sketch and the similar image is reduced, and the feature vector spacing corresponding to the sketch and the heterogeneous image is increased at the same time. Compared with triplet loss, the loss is reduced; quadruplet loss limits the distance between the sketch and the image and pays attention to the heterogeneous spacing of the image at the same time, so that the distribution of different types of images in the final feature space has higher class discrimination, i.e., a larger inter-class distance and a relatively smaller intra-class distance are generated, and thus the retrieval model has better performance.

The invention discloses a macroeconomic analysis method based on internet big data. The method comprises the following steps: crawling target macroeconomic parameters on each network base station by adopting a web crawler module; preprocessing target macroeconomic parameters through an attribute reduction algorithm based on inter-class discrimination; operating a preset macroeconomic parameter classifier and / or a macroeconomic parameter classification algorithm based on Hadoop to realize classification of macroeconomic parameters to obtain a target macroeconomic parameter set; according to theclassification result of the macroeconomic parameters, operating a preset feature parameter extraction model and / or a feature parameter extraction algorithm based on Hadoop to achieve extraction of feature parameters of the target macroeconomic parameter set; and operating a preset macroeconomic analysis model and / or a macroeconomic analysis algorithm based on Hadoop to realize evaluation of thetarget macroeconomic parameter set according to the feature parameters, and outputting a corresponding evaluation result. According to the invention, more accurate and credible analysis can be carriedout on complex macro-economy.

The invention discloses a text feature extracting method based on inter-class distinctness and intra-class high representation degree. The method comprises the following steps: preprocessing a training set text; calculating the class distinctness of each feature word through an improved feature selecting method so as to select feature words with more class representation, wherein the selected feature words are of high distinctness among different classes; and further screening the selected feature words which are of high class distinctness based on the intra-class distribution rate and information gain (IG) of the feature words. With the adoption of the method, the feature selection is carried out twice to select the feature words which are of high intra-class information entropy and high intra-class distribution rate, and thus the classifying efficiency and accuracy can be improved; in addition, the calculation is simple, so that the text classifying speed and accuracy can be improved.

The invention relates to a feature selection method and application based on feature identification degree and independence. The method comprises following steps: calculating the importance degree of each feature by measuring inter-class distinguished ability with feature identification degree and measuring correlational relationship between features with feature independence and sequencing in a descending order; and selecting top k features with the importance higher than those of others to form a feature subset with high class-discrimination performance. Differently-expressed gene subsets selected in application of oncogene expression profile data obtain fine time and class discrimination performance. The feature selection method and application based on feature identification degree and independence have following beneficial effects: easy calculations can be made; time complexity is reduced; selection efficiency runs high; and a good reference is provided for clinical diagnoses and judgments of tumors and other diseases.

The invention relates to a method for optimizing the oil-paper insulation time domain dielectric response feature quantity of a two-stage transformer. The advantages of a filter type feature selectionstatistical index and random forest bag external data feature quantity importance estimation are combined, and an optimal feature space with the lowest redundancy, the highest class discrimination degree and the highest classification importance can be finally determined through two-stage feature selection. The method is used for realizing evaluation selection of the time domain dielectric spectrum feature quantity; under the condition that a traditional insulation diagnosis method adopts a similar dimensional feature space, the method can be used for obtaining more effective information, theaccuracy of insulation diagnosis is greatly improved, a new idea of selecting the feature space is provided for subsequent transformer insulation evaluation by using the multivariate time domain feature quantity, and the method has an important application value in practical engineering.

The invention provides a matrix classification model based on inter-class discrimination, comprising the following steps: collecting a data set, and converting collected samples into matrix samples; constructing a regularization term R<BC>; introducing the regularization term R<BC> to MatMHKS, generating a new matrix-pattern-oriented classification model CBCMatMHKS, training the model with a training set, and using a gradient descent method to solve the model CBCMatMHKS in order to get the optimal solution to the model; testing the optimal solution with a test set to get an optimal decision function; and finally, using the optimal decision function to calculate an input matrix sample of which the class needs to be judged, and classifying the matrix sample according to an output result. Compared with the traditional matrix classification model, the distance between local samples of different classes is maximized by introducing inter-class discrimination information and using the cluster center to represent the samples in a region, and the accuracy of classification is improved.

The invention discloses a target classification method based on a differential chain code histogram and a device thereof. The method comprises the steps that off-line classifier training is performed on a training image set so that a corresponding target class discrimination function and parameters thereof are obtained; prospect target detection is performed on the inputted video sequence images; target characteristic extraction is performed on the detected prospect target; and the extracted target characteristics act as input, and on-line class discrimination is performed on the prospect target in the video sequence images by utilizing the target class discrimination function and the parameters thereof which are obtained via training. First-order differential chain code histogram characteristics of the prospect target are extracted, calculation amount is low and the characteristics are invariant in target translation, scale and rotation.

The invention discloses an early warning method for giving precedence to pedestrians under new traffic rules and an early warning system for giving precedence to the pedestrians under the new trafficrules. The method comprises the steps that vehicle driving information, pedestrian information and traffic sign line information are acquired; the pedestrian information is detected by using a pedestrian detection algorithm based on deep learning so as to complete pedestrian position detection and class discrimination; the traffic sign line information is detected by using a traffic sign line detection algorithm, and the traffic sign line is extracted; and whether the pedestrians have the intention of crossing the road is judged according to the result, the relative position of the pedestriansand the vehicle is analyzed if the judgment result is yes, and an early warning signal is emitted to prompt the vehicle driver to stop to wait for the pedestrians to pass when the vehicle meets the stop rules of giving precedence to the pedestrians. The system comprises a sensor, an embedded platform and an early warning device and is used for performing early warning in case of giving precedenceto the pedestrians under the new traffic rules. According to the early warning method for giving precedence to the pedestrians under the new traffic rules and the early warning system for giving precedence to the pedestrians under the new traffic rules, the driver can be assisted to be clear about the rules of giving precedence to the pedestrians, and the safety of the pedestrians can be guaranteed.

The invention relates to a content-based novel recommendation method, belonging to the technical field of recommendation method. Firstly, the novel text is initialized and the corresponding SinHash fingerprint is extracted to establish the dynamic novel database. Then the reference novel is input and the novel to be recommended is determined by the publishing time and the correlation between the novel texts based on the SinHash fingerprint. Finally, the recommended novels are sorted according to their relevance, and the recommended novels with certain items are outputted. Compared with the prior art, the method mainly solves the problems of low recommendation accuracy, weak class discrimination ability, poor efficiency and the like existing in the prior art when recommending novels, and increases the accuracy and flexibility of relying on computers to recommend novels at present.

The invention discloses a feature visualization method of a deep neural network based on constrained optimization class activation mapping. Obtain a pre-trained model built with a deep neural network for image classification by training or downloading; use the pre-trained model to forward pass an image to be tested to obtain a feature map, and further process to obtain the final weight vector; through the final weight The vector weights and sums the components of the feature map to obtain a visualized feature map, which is presented as the final visualization result. The present invention can perform feature visualization on any deep neural network, can achieve better visualization effect of deep feature interpretability, has less noise and stronger class discrimination.

The invention relates to a content-based novel recommendation method, belonging to the technical field of recommendation methods. First, initialize the novel text and extract the corresponding SinHash fingerprints to establish a dynamic novel database; then input the reference novels, and determine the novels to be recommended by the publication time of the novels and the correlation between the novel texts based on the SinHash fingerprints; finally, the recommended novels are correlated Sort by degree and output the recommended novels of a certain item. Compared with the prior art, the present invention mainly solves the problems of low recommendation accuracy, weak class discrimination ability, and poor efficiency in the prior art when recommending novels, and increases the accuracy and efficiency of recommending novels by relying on computers at present. flexibility.