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177results about How to "Prevent overfitting" patented technology

Unbalanced data classification method based on unbalanced classification indexes and integrated learning

The invention discloses an unbalanced data classification method based on unbalanced classification indexes and integrated learning, and mainly solves the problem of low classification accuracy of the minority class of the unbalanced data in the prior art. The method comprises steps as follows: (1), a training set and a testing set are selected; (2), training sample weight is initialized; (3), part of training samples is selected according to the training sample weight for training a weak classifier, and the well trained weak classifier is used for classifying all training samples; (4), the classification error rate of the weak classifier on the training set is calculated, is compared with a set threshold value and is optimized; (5), voting weight of the weak classifier is calculated according to the error rate, and the training sample weight is updated; (6), whether the training of the weak classifier reaches the maximum number of iterations is judged, if the training of the weak classifier reaches the maximum number of iterations, a strong classifier is calculated according to the weak classifier and the voting weight of the weak classifier, and otherwise, the operation returns to the step (3). The classification accuracy of the minority class is improved, and the method can be applied to classification of the unbalanced data.
Owner:XIDIAN UNIV

Isolated forest-based binary classification abnormal point detection method and information data processing terminal

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.
Owner:CHENGDU UNIV OF INFORMATION TECH

Human face identification method and apparatus

The invention discloses a human face identification method and apparatus, and belongs to the field of human face identification. The method comprises: performing feature extraction on a to-be-identified human face image by using a plurality of pre-trained convolutional neural networks to obtain a plurality of sub-feature vectors of the to-be-identified human face image, wherein the sub-feature vectors of the to-be-identified human face image are same in number of dimensions; normalizing the sub-feature vectors of the to-be-identified human face image; performing addition on the normalized sub-feature vectors of the to-be-identified human face image, and multiplying the sum of the normalized sub-feature vectors by a coefficient to obtain a union feature vector of the to-be-identified human face image; and performing human face identification by using the union feature vector of the to-be-identified human face image or/and the sub-feature vectors of the to-be-identified human face image. According to the human face identification method and apparatus, the training time of the convolutional neural networks is shortened, the over-fitting of the convolutional neural networks is avoided, and the operation is simple and convenient; and identification modes are more diversified and the accuracy is higher.
Owner:BEIJING TECHSHINO TECH

Fine-grained image classification method based on sparse bilinear convolutional neural network

The invention relates to a fine-grained image classification method based on a sparse bilinear convolutional neural network, and the method comprises the steps: carrying out the feature channel cutting of the bilinear convolutional neural network, automatically thinning feature channels in a training process, distinguishing the importance of the feature channels for classification, and carrying out the scale cutting according to the importance; inputting the output of the bilinear convolutional neural network into the batch regularization; taking a scaling factor of BN as a scale factor; applying a regularization method to the scale factor, wherein the regularization method has a plurality of types such as L1 and L2, the sparsity of L1 is strong, the sparsity of the feature channels can be realized by jointly training the network weight and the scale factor; finally, performing pruning according to the size sequence of the sparse scale factor, and finally, obtaining a model for finally performing a fine-grained image classification task by utilizing fine tuning. Weak supervision can be realized, redundant parameters are reduced, overfitting is prevented, and the accuracy of fine-grained image classification is effectively improved.
Owner:UNIV OF SHANGHAI FOR SCI & TECH

Tool abrasion state identification method based on convolutional neural network and long-short-time memory neural network combined model

ActiveCN110153802ANo expert experience requiredSave the cost of selecting featuresMeasurement/indication equipmentsNumerical controlNerve network
The invention discloses a tool abrasion state identification method based on a convolutional neural network and long-short-time memory neural network combined model. A force measuring instrument and an acceleration sensor are arranged on a workbench clamp of a numerical control machine tool and a workpiece, three-direction force signals and vibration acceleration signals are collected, collected data are subjected to data pre-processing, normalization processing and unified segmentation are conducted on the same row of data, one-dimension data are converted into two-dimension data to serve asinput, the convolutional neural network in the combined model is used for extracting abstraction features, the long-short-time memory neural network in the combined model is used for finding relevancebetween the data, and finally the tool abrasion state is output. An established double-network structure is arranged in a serial manner, the internal relation between the two kinds of signals can beestablished, the more abstract features are extracted through convolution, the timing sequence feature is determined according to the long-short-time memory, accordingly, the purpose of deeper relation of the data and the model is achieved, and applicability is achieved on various machine tools.
Owner:SOUTHWEST JIAOTONG UNIV

Target classification and positioning method based on network supervision

The invention provides a target classification and positioning method based on network supervision. The target classification and positioning method comprises the following steps: automatically obtaining a large amount of network image data from a search engine according to the category of a to-be-tested target; filtering to remove noise images to form a training sample set; preliminarily constructing a classification and positioning network; and inputting samples in the training sample set into the preliminarily constructed classification and positioning network to perform feature extraction,classifying the features, obtaining position information of the target object, and training the classification and positioning network. According to the end-to-end fine classification and positioningmethod based on network supervision, massive network images easy to obtain are used as a training set, manual annotation is completely removed, only image-level labels are used, an efficient convolutional network is designed, and algorithms such as global average pooling and class activation mapping graphs are fused, so that the performance of the method exceeds that of a weak supervised learningmethod on fine classification tasks and positioning tasks.
Owner:UNIVERSITY OF CHINESE ACADEMY OF SCIENCES

Method of universal computing device

A method for using artificial neural networks as a universal computing device to model the relationship between the training inputs and corresponding outputs and to solve all problems with estimation, classification, and ranking tasks in their nature. Raw data related to problems is obtained and a subset of that data is processed and distilled for application to this universal computing device. The training data includes inputs and their corresponding results, which values could be continuous, categorical, or binary. The goal of this universal computing device is to solve problems by the universal approximation property of artificial neural networks. In this invention, a practical solution is created to resolve the issues of local minima and generalization, which have been the obstacles to the use of artificial neural networks for decades. This universal computing device uses an efficient and effective search algorithm, Retreat and Turn, to escape local minima and approach the best solutions. Generalization for this universal computing device is achieved by monitoring its non-saturated hidden neurons as related its effective free parameters and In-line Cross Validation process. The output process of ranking is achieved by an added baseline probability retaining from best logistic regression model as a secondary order while the categorical results from a MLP neural network as the first order.
Owner:CHEN HUNG HAN

GAN generated picture detection method and system based on residual domain rich model

The invention provides a GAN generated picture detection method and a system based on a residual domain rich model, and the method comprises the steps: an original image processing step: carrying outthe recognition and cutting of an original image through employing a digital image processing technology, recognizing a face, and cutting an image of a face part; a step of obtaining residual information of the original image: preprocessing the original image by using a digital image processing technology, and extracting the residual information of the original image; a convolutional neural network processing step: inputting a residual image of the original image into a set convolutional neural network, and adding a BN layer to a convolutional layer in front of each activation function; a global average pooling layer processing step: replacing a full connection layer with a global average pooling layer; and a sample training convolutional neural network processing step: training a convolutional neural network by using the sample of the data set to obtain a picture classifier, and obtaining judgment result information. According to the method, a preprocessing high-pass filter is designed, and 99% of accuracy is finally achieved through an improved convolutional neural network.
Owner:SHANGHAI JIAO TONG UNIV

Epileptic focus three-dimensional automatic positioning system based on deep learning

ActiveCN110390351ATaking into account the asymmetry of metabolic distributionPrecise positioningImage enhancementImage analysisNetwork modelMirror image
The invention discloses an epileptic lesion three-dimensional automatic positioning system based on deep learning. The system comprises: a PET image acquisition and marking module; a PET image and standard symmetrical brain template registration module; a PET image data preprocessing module wich is used for generating mirror image pairs of the left and right brain image blocks; a twin network SiameseNet training module which comprises two deep residual convolutional neural networks sharing weight parameters, wherein an output layer is connected with a multi-layer perceptron and a softmax layer, and the networks are trained by using a training set carrying images of epileptic lesions and normal images to obtain a network model; and a classification module and an epileptic focus positioningmodule which are used for generating a probability heat map from a newly input PET image by utilizing the trained network model, judging whether the image is normal or carries an epileptic focus sample through a classifier, and then predicting the position of an epileptic focus area. According to the system, the epileptic focus of the PET image is automatically positioned by introducing the mirrorimage pair of the image block and the twin network SiameseNet, so that the positioning accuracy and efficiency of the epileptic focus can be effectively improved, and the robustness is relatively high.
Owner:ZHEJIANG UNIV

A construction method and application of a lightweight gesture detection convolutional neural network model

InactiveCN109902577AOccupies less computing resourcesSolve the technical problem that it is difficult to obtain a large amount of high-quality gesture image dataCharacter and pattern recognitionNeural architecturesData setMulti targeting
The invention relates to a construction method and application of a lightweight gesture detection convolutional neural network model, and the method comprises the steps: constructing a lightweight gesture detection convolutional neural network framework based on a SquezeNet convolutional neural network framework and an SSD multi-target detection convolutional neural network framework; Acquiring agesture picture and a background picture, and performing image data enhancement and picture synthesis processing on the gesture picture based on the background picture to obtain a gesture data set; And based on the public data set and the gesture data set, training a lightweight gesture detection convolutional neural network framework to obtain a lightweight gesture detection convolutional neuralnetwork model. According to the invention, a small amount of gesture data is expanded into the gesture data set containing a large amount of picture data at a high speed; The technical problem that alarge amount of high-quality gesture picture data is difficult to obtain is solved, in addition, by combining the SquezeNet convolutional neural network architecture and the SSD multi-target detectionconvolutional neural network architecture, the constructed lightweight gesture detection convolutional neural network model occupies few computing resources, and can be applied to various detection platforms.
Owner:HUAZHONG UNIV OF SCI & TECH

Machine tool cutter residual life prediction method based on LSTM + CNN

The invention discloses a machine tool cutter residual life prediction method based on LSTM + CNN, and the method comprises the steps: carrying out the judgment of the signal features of uploaded training data, and distinguishing a continuous signal and a discrete signal; performing data merging on the real-time data of different frequencies sampled by the sensor; checking whether missing values or abnormal values exist in the training data and the real-time data or not; if the missing values or the abnormal values exist, using a moving average method to supplement the missing values or replacing the abnormal values, so as to enable the data to be complete and effective, and removing outliers; carrying out selection and dimension reduction on the training data and the real-time data according to data characteristics so as to facilitate model fitting and prevent an over-fitting phenomenon; and training and testing the LSTM + CNN model, and adjusting training parameters and model parameters according to the error, so as to reduce the error to a reasonable range. According to the method, the precision of the prediction result is improved by adopting a grouping mode and a dimension reduction mode, deterministic factors and uncertain factors are comprehensively considered, and the precision of the prediction result can be effectively improved.
Owner:SHANDONG INSPUR GENESOFT INFORMATION TECH CO LTD
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