811 results about "Binary classification" patented technology

The invention belongs to the technical field of computers, and discloses a machine translation method and system based on a generative adversarial neural network. The method comprises the following steps that: on the basis of an original machine translation generation network, a discrimination network which generates network countermeasure with the original machine translation generation network is imported; a translation used for judging a target language is from a training parallel corpus and is a network machine translation result of the original machine translation generation network; and the discrimination network adopts a multi-layer sensor feedforward neural network model to realize binary classification. The system comprises the discrimination network, a generation network, a mono-lingual corpus and a parallel corpus. While manually annotated bilingual parallel corpus resources are fully utilized, and mono-lingual corpus resources also can be fully utilized to carry out semi-supervised learning; and the mono-lingual corpus resources are very rich and can be easily obtained, and the problem that required training corpora required by the neural network machine translation model are not sufficient is solved.

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

The invention relates to a mobile robot real-time layered path planning method based on a grid map. The method, which is used in a grid map, comprises the following steps: carrying out binary classification according to external environment information, and establishing a grid map; planning an outer path according to a speed-based artificial potential field method; and when the path is about to fall into local minimum and oscillation, planning an inner path according to an A star algorithm. The problem that the traditional artificial potential field method may fall into local minimum and planned path oscillation and the problem that the A star algorithm is not applicable to real-time path planning are solved. Real-time, safe and stable operation of mobile robots is ensured.

The invention discloses an image type fire flame identification method. The method comprises the following steps of 1, image capturing; 2, image processing. The image processing comprises the steps of 201, image preprocessing; 202, fire identifying. The fire identifying comprises the steps that indentifying is conducted by the adoption of a prebuilt binary classification model, the binary classification model is a support vector machine model for classifying the flame situation and the non-flame situation, wherein the building process of the binary classification model comprises the steps of I, image information capturing;II, feature extracting; III, training sample acquiring; IV, binary classification model building; IV-1, kernel function selecting; IV-2, classification function determining, optimizing parameter C and parameter D by the adoption of the conjugate gradient method, converting the optimized parameter C and parameter D into gamma and sigma 2; V, binary classification model training. By means of the image type fire flame identification method, steps are simple, operation is simple and convenient, reliability is high, using effect is good, and the problems that reliability is lower, false or missing alarm rate is higher, using effect is poor and the like in an existing video fire detecting system under a complex environment are solved effectively.

The invention discloses a deep residual network-based semantic mammary gland molybdenum target image lump segmentation method. The method comprises the following steps of: labelling pixel categories of lumps and normal tissues corresponding to a collected mammary gland molybdenum target image so as to generate label images, and dividing the mammary gland molybdenum target image and the corresponding label images into training samples and test samples; preprocessing the training samples to form a training data set; constructing a deep residual network, and training the network by utilizing thetraining data set, so as to obtain a deep residual network training model; after a to-be-segmented mammary gland molybdenum target image lump is preprocessed, carrying out binary classification and post-processing on a pixel of the to-be-segmented mammary gland molybdenum target image by utilizing the deep residual network training model, and outputting lump segmentation image to realize semanticsegmentation of the mammary gland molybdenum target image lump. The method is capable of effectively improving the automatic and intelligent levels of mammary gland molybdenum target image lump segmentation, and can be applied to the technical field of assisting radiologists to carry out medical diagnosis.

The invention provides a mammary gland lesion area detection method based on deep learning and transfer learning. The method comprises: preparation and amplification of a training set and a test set;according to lump position information marked by a doctor in the breast data set, extracting an available lump image and normalizing the size of the available lump image into a size of 100 * 100 pixels as a positive sample; according to the invention, the AlexNet network is used to train the parameter model of the classification model of the natural image on the ImageNet data set; training and transfer learning are carried out on a specific breast image data set, so that the binary classification problem of the convolutional neural network on a small-scale breast data set can be successfully solved, a lesion area in the breast image can be identified, and the prediction effect on the breast lesion is improved.

The invention discloses a pedestrian identification method of a camera network based on multi-level depth feature fusion. A new network model is learnt on a pedestrian database by migrating parameters of a pre-training network to the pedestrian database, a plurality of multi-level depth features are extracted using the new network model, and then a Softmax classifier in the last layer of a convolutional neural network is replaced by an SVM classifier to achieve the purpose of making full use of the multi-layer depth features. Furthermore, the multi-level depth features are used for constructing a plurality of groups of SVM classifiers of binary classification and decision values of these binary classifiers are linearly weighted to obtain final classification results. The invention can effectively improve the accuracy of recognizing a pedestrian target by way of multi-level feature fusion in a decision-making layer of the SVM classifier.

The invention relates to a defect identification method for a solar panel based on a convolution neural network (CNN). The method comprises the two stages of model off-line training and on-line detection. CNN models are applied to defect identification of the solar panel, and defect detection and classification are progressively realized by two CNN models. Firstly, a CNN binary classification model is used for distinguishing qualified and defective images, and then a CNN multi-classification model is used for classifying images which are classified as defects by the binary classification model. The CNN models adopt the same processing flow for various defect types of the solar panel, namely, feature extraction and feature classification are performed rapidly and automatically through iterative training. For a new defect type, detection of the defect type can be realized by only collecting sample data of the defect type, adding the sample data into a training data set and training the models. Through adoption of the defect identification method, the location of a small defective solar panel can be identified at relatively high accuracy. Moreover, the method can classify various defects, so that the applicability of the method is wider.

The invention discloses a scholar name duplication disambiguation method which comprises the steps of classification model establishing and iteration disambiguation. In the classification model establishing step, a labeled data set is obtained by labeling based on heterogeneous academic network data, a document-to-binary-classification training data set is established based on the labeled data set, binary classification model training is carried out through a classification algorithm based on the training data set, and a document-to-binary-classification model is obtained. In the iteration disambiguation step, iteration judging is carried out on the data set which needs disambiguation with an iteration classification algorithm based on a binary classification model, a final agglomeration corresponding to a real scholar is obtained, and scholar name duplication disambiguation processing is achieved. The invention further discloses a scholar name duplication disambiguation system.

The invention provides a semi-supervised high-resolution remote sensing image scene classification method based on a generative adversarial network. The method comprises: constructing an EMGAN model:changing the discriminator of the generative adversarial network from binary classification to multi-classification to obtain an EMGAN discriminator, and adding an information entropy maximization network to the generator of the generative adversarial network to obtain an EMGAN generator; training an EMGAN model; dividing a loss function of the EMGAN discriminator into a supervision part and an unsupervised part according to whether a training image has a label or not; dividing a loss function of the EMGAN generator into a feature matching loss function and a generated image information entropy loss function; alternately training the EMGAN discriminator and the EMGAN generator; finely adjusting the VGGNet-16 model; training an SVM model; and fusing the features of the EMGAN model and the VGGNet-16 model, and performing scene classification to obtain a classification result. The remote sensing image scene classification method can effectively improve the precision of remote sensing image scene classification under the condition of few training samples.

The invention discloses a method for automatically classifying academic documents, which comprises the following steps: inputting training documents into a database, wherein the training documents comprise document classification numbers; selecting unitary characteristic words and binary characteristic words, and generating binary word pairs for the training documents; reading the training documents in the database, and respectively calculating the probability relations between the unitary and binary characteristic words and the document classification numbers, thereby forming a unitary classification dictionary and a binary classification dictionary; reading a document to be labelled, calculating the Chinese library classification number corresponding to the document according to the weight of the unitary and binary classification dictionaries, and the unitary and binary characteristic words in the document to be labelled, and automatically labelling; and dividing the unitary and binary classification result into a high-accuracy result set and a low-accuracy result set according to the degree of confidence, and outputting the classification result.

a method for improving stacking schema for classification tasks, according to which predictive models are built, based on stacked-generalization meta-classifiers. Classifications are combined to build a new scheme from at least two layers and multiclass classification problems are converted into binary classification problems. One-against-all class binarization and regression learners are used for each class model and ensemble classifiers are improved using stacking. Accuracy differences, accuracy ratio, and runtime classification in multiclass datasets are also improved and the class of a value is then predicted.

The invention relates to a defect detection and classification method based on FCNs (fully convolutional networks) and applied to galvanized stamping parts. The method comprises steps as follows: collecting samples with various types of defects; performing preliminary binary classification according to image gray standard deviation, and distinguishing qualified workpieces and defective workpieces;preprocessing the preliminarily screened samples to improve contrast, and extracting region of interests to serve as improved FCN inputs for training; calculating pixel values of output workpiece images, and setting a threshold value to judge the types of the workpiece defects and performing classification. Defect detection and classification are performed through combination of image processingand the FCNs, so that complicated preprocessing and feature extraction are avoided, and the defects of the galvanized stamping parts can be better detected and classified.

An improved system and method is provided for determining web page quality using collective inference based on local and global web page information. A classification engine may be provided for classifying a web page using local features of a seed set of web pages and global web graph information about the seed set of web pages. A dual algorithm based on graph regularization formulated as a well-formed optimization solution may be used in an embodiment for applying collective inference for binary classification of the web page using the local web page information and global web graph information of a web page, the local web page information and global web graph information of an authoritative set of web pages, and the local web page information and global web graph information of a non-authoritative set of web pages.

The invention relates to a secure steganography method and device based on generative adversarial networks. The method includes: generating a carrier image, into which information needs to be embedded, through the generator network under a generative adversarial network framework, and judging authenticity of the generated carrier image through the discriminator network, enabling the carrier image,which is generated by the generator network, to be close to a real image through a dynamic game process of the generator network and the discriminator network; carrying out embedding of the information on the carrier image generated by the generator network; and then utilizing a steganalysis network to carry out binary classification on the input carrier image and a steganographed image to obtainaccuracy rates of classification into an original image and the steganographic image. According to the method, the generated carrier image is closer to the real image visually, a generation speed isfaster, and steganographic security can be improved.

The invention belongs to the technical field of communication control and communication processing, and discloses an isolated forest-based binary classification abnormal point detection method and aninformation data processing terminal. The method comprises the steps of carrying out initial static average blocking on an original data set, and calculating the density in the block and the mean density; after calculating the density in each block of the static block, reducing the data set by taking the mean density of the original data set as a threshold value; constructing an isolated forest byusing a node recursion method; performing corresponding feature extraction and datamation on the original data set, and calculating the spatial position distances between the clustering center pointand other points; adding the abnormal score calculated on the basis of the density and the distance and the abnormal score calculated on the basis of the proof information and comparing with a corresponding threshold value. According to the method, the accuracy of an abnormal point detection algorithm is effectively improved, the actual data size in the abnormal detection process can be greatly reduced, the calculation resources are saved, and the abnormal detection efficiency is improved, and the robustness of an abnormal detection algorithm is enhanced.

The invention relates to a method for automatically filtering a defective image based on multilayer feature. The preset filtering method has poor effect. The method comprises the following steps: inputting an image, preprocessing the input image and carrying out complexion detection on the preprocessed image to obtain a complexion mask image; extracting first-layer features of the images, classifying the images by adopting a first-layer binary system classification tree, carrying out trunk positioning on a suspected image and outputting a classification result; carrying out truck positioning on images which are successfully positioned by adopting a human face detection method; if the positioning is succeeded, extracting a third-layer feature and outputting an image by adopting a decision tree classifier; and positioning a truck of a human body by adopting an elliptical fitting method for the images which are not successfully positioned; after the feature is extracted, outputting classification results by adopting a binary system classification tree and finishing. According to the invention, internet industries carrying more multimedia information such as network video, social network sites, and the like can be healthily and sustainably developed.

The invention relates to a method for the training of a classifier based on weakly labeled images and for the binary classification of an image. The training of the classifier comprises the steps of automatically and iteratively determining initial regions of interest for a training set and further on refining said regions of interest and adapting the classifier onto the refined regions of interest by a classifier refinement procedure. Further on, for a query image with unknown classification, an initial region of interest is determined and refined as to maximize the probability value derived at the output of said classifier. The query image is automatically assigned a negative classification label if said probability value is lower than or equal to a predetermined first threshold. The query image is automatically assigned a positive classification label if said probability value is greater than a predetermined second threshold.

The invention discloses a satellite image segmentation method based on residual network and U-Net segmentation network. The satellite image segmentation method comprises the following steps: constructing a residual network ResNet 34; constructing U-Net segmentation network; constructing a training sample set; training the residual network ResNet 34; training the U-Net segmentation network; inputting the satellite image to be segmented into the residual network ResNet 34 for binary classification, and judging that a ship target is included; using the U-Net segmentation network to perform binarysegmentation on the positive samples in the classification result; for the negative samples in the classification result, directly outputting a single-value mask graph. According to the method, the satellite image is subjected to binary classification by using the residual network ResNet 34, and the U-Net segmentation network is used to only segment the positive samples in the classification result, and an SE-ResNet module is embedded in the U-Net segmentation for extracting finer segmentation masks and the satellite image segmentation method is high in real-time performance and segmentationprecision.

The invention relates to a target detection method of a convolutional neural network based on pyramid input gainconvolutional neural network target detection method based on pyramid input gain, and belongs to the technical field of computer vision and target detection. The target detection method is based on a convolutional neural network model PiaNet comprising a feature extraction module and a multi-task prediction module. The target detection method comprises a training stage and a test stage. A two-stage transfer learning strategy is adopted in the training stage, and the method comprisesthe steps of (1) data enhancement and data preprocessing, and a training set trained in the first stage, a training set trained in the second stage and a test set are generated; S; step (2), carryingout first-stage training in the binary classification network; (3) carrying out second-stage training to obtain a trained PiaNet network; I; in the test stage, a target is accurately detected, specifically, a test set is input into a trained PiaNet network, and the position of a detection box and a classification result are output through a multi-task loss function. And the method is wide in application range and has very high robustness.