81results about How to "Solve overfitting" patented technology

The invention discloses a graphic pattern text detection method based on deep learning. The method includes firstly, training a depth convolution self-encoding network by combining graphic pattern text samples, than adopting marked samples, and classifying through a sparse dictionary; extracting graphic pattern texts from a sample library, rotating, shifting and transmitting, and combining the graphic pattern texts with pure background graphics; adopting a combined sample seat, establishing a depth convolution self-encoding network, and learning characteristic templates in layered training and entire optimizing manners; performing characteristic extraction on the characteristic templates acquired by deep network learning according to the acquired marked samples; sampling the extracted characteristics in the size of the original graphics, adopting single blocks as identifying units, and training the sparse dictionary and a classifier; after training, performing multi resolution decomposition on the graphics to be processed, utilizing the characteristic templates to extract characteristics, and utilizing the sparse dictionary to acquire results in a classified manner.

The invention relates to a soft sensing method for load parameters of a ball mill. The method is that a hardware supporting platform is used to obtain vibration signals, vibration sound signals and current signals of a ball mill cylinder to soft sense ball mill internal parameters (ratio of material to ball, pulp density and filling ratio) characterizing ball mill load. The method comprises the following steps that: the vibration, the vibration sound, the current data and the time-domain filtering of the ball mill cylinder are acquired, time frequency conversion is conducted to the vibration and the vibration sound data, kernel principal component analysis based nonlinear features of the sub band of the vibration and the vibration sound data in frequency domain are extracted, nonlinear features of the time domain current data are extracted, feature selection is conducted to the fused nonlinear feature data and a soft sensing model based on a least squares support vector machine is established. The soft sensing method of the invention has the advantages that the sensitivity is high, the sensed results are accurate, the practical value and the popularization prospect are very good, and the realization of the stability control, the optimization control, the energy saving and the consumption reduction of the grinding production process is facilitated.

The invention provides a recommendation model training method and device and a recommendation method and device. The training method of the recommendation model comprises the steps of determining first initial parameter values of training parameters in a to-be-trained recommendation model, wherein the first initial parameter values are target parameter values obtained after initial parameter values are iteratively updated by a click rate estimation model trained in advance; acquiring user characteristics of at least two sample users and attribute characteristics of at least two sample application programs; generating a positive sample of that the sample users purchase the exposed sample application programs and a negative sample of that the sample users do not purchase the exposed sample application programs based on the user characteristics and the attribute characteristics; and on the basis of a sample set comprising at least one positive sample and at least one negative sample and the first initial parameter value, training a to-be-trained recommendation model to obtain the recommendation model, and outputting the exposure conversion rate of each sample user on each sample application program by the recommendation model.

The invention provides an object-oriented high-resolution remote-sensing image classification method. The method comprises the steps of S1, conducting segmentation processing on images to be processed to obtained a plurality of subimage objects; S2, obtaining feature information of subimage objects; and S3, classifying subimage objects according to the obtained feature information, wherein images to be processed are high-resolution remote-sensing images, the feature information of subimage objects comprises spectral information, shape information and texture information of subimage objects. According to the method, on the basis of object-oriented classification, a classification method combining probabilistic latent semantic analysis and a support vector machine is introduced, the problem that 'the same features with different classifications' and 'the same classifications with different features' are not high in identification ratio in the prior art is solved, the classification precision of high-resolution remote-sensing images is greatly improved, advantages of latent semantic analysis (LSA) and advantages of probabilistic latent semantic analysis (PLSA) are combined, and the problems of overfitting and local optimum which are caused by random initialization are effectively solved.

The invention discloses a migration convolution neural network method for face expression recognition, which comprises the following steps: S1, acquiring a face expression image data set, dividing thedata set into a training set, a verification set and a test set; 2, cascade that migration network and the convolution neural network to construct a migration convolution neural network model; The input of MCN model is facial expression image data, and the output is facial expression category. 3, train that migration convolution neural network model by using a training set, and optimize the trained migration convolution neural network model by using a verification set; S4, the facial expression recognition accuracy of the test set is tested by the optimized migration convolution neural network model. The invention utilizes the migration convolution neural network to recognize the facial expression, and solves the problems that the small data set cannot be converged and over-fitted on a large number of networks.

The invention provides an automatic blog writer interest and character identifying method based on a support vector machine. The automatic blog writer interest and character identifying method includes building an interest classified training sample set and a character classified training sample set at first; respectively processing the two training sample sets by a Chinese morphology analyzer to obtain a candidate interest feature item set and a candidate character feature item set; then analyzing the two candidate feature item sets by the aid of a statistics method; building an interest classified feature item set and a character classified feature item set; displaying the interest classified training sample set and the character classified training sample set into vector forms by the two feature item sets; and finally respectively using two groups of training interest classifiers and character classifiers. The classifiers are used for identifying interests and characters of other writers. By the aid of the automatic blog writer interest and character identifying method, the interests and the characters of the writers can be accurately identified, the method is applied to various personal services based on information of the writers, service providers can sufficiently know users, service quality is improved, and the method has an extremely high practical value.

The invention provides a network flow prediction method and device based on a cognitive network. The method comprises the following steps of: carrying out least square method processing on an input signal X(t); outputting prediction sample data Y(t); carrying out wavelet transformation on the Y(t); decomposing the Y(t) into components with different frequency compositions; carrying out wavelet transformation on a coefficient sequence {D1(k), D2(k), ...... DL(k), AL(k)} at the k moment; training the network with the component {D1(k), D2(k), ...... DL(k)} as input of an Elman network and a wavelet coefficient {D1(k+T), D2(k+T), ...... DL(k+T)} at the k+T moment as output; training the network with the component of {AL(k)} as input of a linear network and {AL(k+T)} as output; training the network with each trained wavelet component {D1(k+T), D2(k+T), ...... DL(k+T), AL(k+T)} as input of a BP network and the original flow time {f(k+T)} at the k+T moment as the network output; obtaining the prediction output; introducing an LMS (Least Mean Square) algorithm to pre-process the input sample aiming at advantages and disadvantages of the traditional flow model and prediction method; inputting the input sample to a WNN (Wavelet Neural Network) prediction model, therefore, the over-fitting problem in the traditional model is solved, and a more accurate model and prediction are provided for the network flow.

The invention discloses a training method of an image classification network, an image classification method and device and a server. The training method comprises the following steps: preparing a data set of pictures with labels as input in advance; Constructing corresponding hierarchical neural network structures according to different classification levels; And carrying out hierarchical training on each hierarchical neural network structure to obtain a parent class corresponding to the maximum probability value and probability values of the input pictures under the parent class belonging todifferent subclasses. According to the method and the device, the technical problem of overfitting caused by redundancy of a full connection layer due to very large data set classification data is solved. Through the training method provided by the invention, the phenomena of low network training speed and over-fitting of the network caused by too many full connection layer parameters are solved.According to the image classification method provided by the invention, hierarchical training is adopted, so that a subclass classification result can be more accurately obtained on a classificationresult of a parent class, and accurate classification is realized.

The invention discloses an encrypted traffic identification method oriented to category imbalance. The method comprises the following steps: S1, acquiring a data set; S2, balancing the data set: processing the original experimental data set by adopting an improved SMOTE algorithm based on density estimation; S3, data preprocessing: reading a data stream, cutting off data, and carrying out normalization processing; S4, optimizing the feature set: automatically extracting features through a variational automatic encoder model, identifying features commonly used in the field through network traffic, and obtaining the optimal feature set by using a feature selection method based on a tree model; S5, identifying flow: inputting the optimal feature set into a random forest CGA-RF classifier algorithm improved based on a genetic algorithm, and identifying target encrypted flow; and S6, analyzing the obtained index result, and optimizing an encrypted traffic identification method. The method is high in recognition rate and low in false alarm rate, and is suitable for encrypted traffic recognition of data set category imbalance and difficult feature extraction.

The invention discloses a method of dynamically predicting a middle and short term area pediatric outpatient amount based on a time sequence. A middle and short term pediatric outpatient amount in an area is dynamically predicted accurately so that a scientific basis is provided for solving problems of area health resource programming, optimization configuration and the like. Simultaneously, through introducing different disease outpatient amount distribution, abnormal states, such as sudden infectious diseases and the like, can be determined so that spreading of the infectious diseases in groups can be rapidly prejudged. The method is realized based on a time sequence prediction model. The method comprises the following steps of step1, sequence smoothness inspection and processing; step2, event variable identification; step3, parameter estimation and inspection; step4, optimal sequence length identification; and step5, dynamic prediction. The above steps are mainly used for selecting an optimal sequence prediction model, a time variation influence is eliminated, problems of overfitting of time sequence prediction and a poor extrapolation effect are effectively overcome and accurate prediction is performed.

The invention discloses a deep reinforcement learning algorithm based on time sequence difference, which is used for solving a hybrid flow shop scheduling problem of a related parallel machine, combines a convolutional neural network with TD learning in reinforcement learning, and performs behavior selection according to input state features; and therefore, the scheduling decision process of the actual order response type production and manufacturing system is better met. A scheduling problem is converted into a multi-stage decision problem, a convolutional neural network model is used to fit a state value function, manufacturing system processing state characteristic data is input into a model, a time sequence difference method is used to train the model, a heuristic algorithm or an allocation rule is used as a scheduling decision candidate behavior, and a reinforcement learning reward and punishment mechanism is combined, thus selecting an optimal combined behavior strategy for each scheduling decision. Compared with the prior art, the algorithm provided by the invention has the advantages of strong real-time performance, high flexibility and the like.

The present invention provides a QRS wave group classification method based on a support vector machine. The method comprises the following steps: a) selecting an appropriate QRS wave group, and performing preprocessing of the data; using a support vector machine to perform training a classification model between each two classes of data; reading QRS wave group data required predicting the type of the waveform; c) performing preprocessing of the data, wherein the preprocessing comprises normalization processing and data dimension algorithm processing; d) performing prediction of the type of the waveform through adoption of the classification model; and e) using a voting method to realize identification and classification of a plurality of forms of QRS wave groups, and obtaining a prediction result. Through random selection of a training set, the QRS wave group classification method based on the support vector machine performs repeat training and test and finally obtain an optimal classification model so as to realize identification of different forms of the QRS wave groups.

The invention discloses a vehicle re-identification method and system based on a double attention mechanism. The method comprises the following steps: constructing a convolutional neural network for vehicle feature extraction; constructing channel attention parts for paying attention to different channels; constructing a granularity attention component used for paying attention to the feature suitable granularity; randomly selecting multiple types of vehicles in each training batch, and randomly selecting multiple images in each type of vehicles to construct batch images; carrying out real-time data enhancement on the batch images and then inputting the batch images into a convolutional neural network; constructing a cross entropy loss function and a triple loss function after smooth regularization of the batch labels, and adding the cross entropy loss function and the triple loss function to obtain an integral loss function; and carrying out feature extraction on the trained convolutional neural network, calculating Euclidean distances between the features, and reordering the distances to obtain a vehicle re-identification result. According to the invention, the fine-grained features of the vehicle image can be better obtained, and the precision and stability of the model are improved.

The embodiment of the invention discloses a training method, device and equipment of an image generator and a storage medium, and relates to the technical field of artificial intelligence such as deeplearning, big data processing, data enhancement and computer vision. The method comprises the following steps: respectively inputting codes of a first modal image and a second modal image of the sameobject into an initial model of an image generator to obtain a first image and a second image; respectively inputting the first image and the second image into a target network model to obtain a feature value of the first image and a feature value of the second image; and determining a loss function based on the feature value of the first image, the feature value of the second image and the typeof the target network model, and adjusting the parameters of the image generator to obtain a trained image generator, thereby providing an image generator capable of generating a specific modal imagein the first modal image and/or the second modal image. The problem of overfitting caused by too few training samples of a specific modal image recognition model is solved, and the recognition precision of the model is improved.

The invention discloses a dynamic safety assessment method based on an extreme gradient boosting decision tree. The method comprises the steps: 1, establishing an original sample set; 2, forming an intermediate sample set; 3, forming an efficient sample set; 4, performing offline training on the extreme gradient boosting decision tree by utilizing the efficient sample set, and constructing a dynamic safety evaluation model based on the XGBoost decision tree; 5, due to the influence of various operation factors of the power system, updating the efficient sample set through time domain simulation, and updating aDSA model by using the updated sample set; 6, based on the real-time measurement data of the synchronous phasor measurement unit, performing online DSA on the power system by using the DSA model. The invention aims to provide a dynamic safety assessment method based on an extreme gradient boosting decision tree. According to the method, online DSA can be efficiently and accuratelycarried out on the power system, the robustness of the evaluation model is good, and the method can adapt to the change of a network topology structure.

The invention relates to an online public opinion early warning method based on an LSTM model. The method is mainly characterized in that the method is based on an online public opinion early warningindex system and a long-short-term memory network model; the invention provides an index system comprising five dimensions of theme attributes, propagation and diffusion and the like. According to themethod, a Re-LSTM (Regulation Long Short Term Memory, long and short term memory network) model is provided on the basis, thus constraining the input weight of each unit in the network by using a regularization method, and replacing a tanh activation function with a softsign function. The method is reasonable in design, and compared with other classic models, the model constructed this time can improve the early warning accuracy and can better avoid the problems of gradient disappearance and overfitting.