792 results about "Cross-validation" patented technology

The invention relates to a tag noise correction based crowd-sourced tagging data quality improvement method. The method comprises the following steps: running a tag integration algorithm in an initial crowd-sourced tagging data set to form a data set after tag integration, and estimating tagger quality and integrated tag quality information of samples in the process; performing multi-round K-fold cross validation by utilizing the data set after tag integration, and constructing a high-quality data set; determining a tag noise set in combination with the tagger quality and the tag quality of the samples by utilizing a prediction probability of a class tag of each sample in the multi-round K-fold cross validation process; and training a classification model by utilizing the high-quality data set generated in the multi-round K-fold cross validation process, and performing prediction and replacement on the class tag of each sample in the tag noise data set by using the model. With the tag noise correction method, the quantity of potential noise tag samples in the data set after original tag integration is reduced, so that the data quality is improved.

The invention discloses a data-based qualitative analysis method of influence factors of circuit breaker faults. The method solves prior problems of insufficient utilization of circuit breaker fault data, too subjective positioning of fault causes, insufficient stability of a qualitative analysis model and the like. According to the method, the key influence factors of all types of the circuit breaker faults are found through mining and analyzing the circuit breaker fault data. The method includes the realization steps of defining an original fault data set; cleaning the fault data; transforming the fault data; reducing the fault data; constructing a qualitative analysis model of the influence factors of the faults, and carrying out ten times of ten-fold cross-validation; and obtaining association rules between the faults and attributes, and obtaining the influence factors of all the types of the circuit breaker faults by qualitative analysis. According to the method, a supervised learning algorithm of CMAR is utilized for modeling, and with data amount increasing, the accuracy of the model will be continuously improved. At the same time, the robustness of the qualitative analysis model is guaranteed by the ten times of the ten-fold cross-validation, and the influence factors of the circuit breaker faults can be effectively analyzed.

The invention discloses an Adaboost software defect unbalanced data classification method based on improvement, and mainly solves the problem that an existing software defect data classification method is poor in classification effect on minority classes. The method comprises the following steps that 1, software data is acquired from a software data set and then preprocessed, software module data is divided into a training set and a testing set for training and testing, and cross validation is performed for ten times; 2, feature selection of the software data is performed by combining a genetic algorithm based on improvement with a BP neural network to obtain an optimal feature subset, and then dimension reduction processing is performed on the software features; 3, the unbalancedness of the software defect data is fully considered, and an Adaboost classifier based on improvement is trained to classify software modules. According to the Adaboost software defect unbalanced data classification method based on improvement, the classification precision of the minority classes can be improved, and the software defect modules can be better detected.

The invention belongs to the field of milk product analysis and particularly relates to a spectral fingerprint identification method for milk, mare milk, camel milk, goat milk and buffalo milk. The invention relates to the intermediate infrared spectroscopy. The method comprises the following steps: 1) taking fresh milk, mare milk, camel milk, goat milk and buffalo milk samples; 2) scanning the samples in a mid-red spectral range to obtain mid-infrared spectral data; 3) preprocessing original mid-infrared spectrum to remove abnormal values; 4) dividing the preprocessed data set into a trainingset and a test set according to a stratified sampling principle; 5) performing dimension reduction processing on the training set to improve the model training speed; 6) establishing a model for distinguishing milk, mare milk, camel milk, goat milk and buffalo milk through 10-fold cross validation by using algorithms such as nearest neighbors on the training set, and evaluating and screening themodel by using the use accuracy, the balance precision and the Kappa coefficient; and 7) estimating the generalization performance of the optimal model.

The invention relates to a high-resolution satellite data-based TRMM rainfall data downscaling method. The method comprises the following steps of: firstly obtaining multiple pieces of satellite meteorological data through an official way, and correcting TRMM remote sensing rainfall data through a Leave-one-out cross validation method; and selecting a variable through step regression according tofactors influencing the rainfall data, considering hysteresis quality, for vegetation, of rainfall and spatial correlation of the vegetation, establishing a multi-spatial scale model, and selecting anoptimum model to downscale the rainfall data. The method is simple in process and correct in rainfall prediction.

The invention discloses a satellite precipitation data correction method based on multi-source information fusion and downscaling. The method comprises the following steps: S1) acquiring live precipitation data and a satellite inversion precipitation product; S2) carrying out optimal interpolation according to the real-time rainfall data and a satellite inversion rainfall product; S3) obtaining low spatial resolution NDVI data; S4) selecting an environment variable and low spatial resolution NDVI data, and performing independent variable screening; S5) establishing a GWR downscaling model; S6)utilizing the GWR model to obtain OI-GWR corrected rainfall grid point data of the research area; and S7) checking the OI-GWR corrected precipitation grid point data of the research area by adopting10-fold cross validation. According to the method of the invention, optimal interpolation is introduced to improve the quality of original satellite precipitation data, and a better initial field is obtained; on the basis that an initial field is improved, a GWR method is used for downscaling, and the spatial resolution and precision of obtained downscaled data are improved.

The invention belongs to the technical field of soil detection, and discloses a soil moisture prediction method based on a machine learning algorithm. The method comprises the following steps: establishing a soil moisture data and prediction factor database; soil moisture simulation prediction and time-space characteristic detection: establishing a soil moisture prediction model in an R language environment by adopting a support vector machine, a random forest and a BP neural network algorithm; performing comprehensive evaluation and space-time characteristic analysis on the soil moisture by adopting a related statistical analysis method; analyzing the prediction model through cross validation and other methods, and evaluating the precision of the method through precision indexes; and determining the importance of the soil moisture predictor. According to the method, prediction factors of terrain, weather, soil, vegetation and the like are selected in combination with local actual conditions, and a novel algorithm-machine learning algorithm is introduced to construct a model to predict the soil moisture, so that accurate prediction of the local soil moisture is realized.

The present invention provides a video semantic analysis method. The method comprises the following steps: S1, performing preprocessing of a video training set, and constructing a sparse linear decoder; S2, adding topological property constraints to build a topology linear decoder, and performing image blocking processing of the video training set to train the topology linear decoder; S3, taking the parameters of a trained topology linear decoder as initial parameters of a convolution layer in a convolution nerve network; and S4, building a key frame set to perform fin adjustment of the convolution nerve network based on the video training set through adoption of a multifold cross validation mode, and building a general feature extractor on the base of the video data, and inputting the features extracted on the training set and a test set to support the video semantic classification in a vector machine. The model training method provided by the invention has video-type data samples capable of responding to various contents so as to improve the accuracy and the robustness of the model.

The invention discloses a method for determination of the polychlorotrifluoroethylene (PCTFE) content in a polymer-bonded explosive (PBX) by near infrared spectroscopy. The method includes the steps of: preparing and collecting 260 PBX samples, taking 180 samples of them as a calibration set for establishing a calibration model, taking the remaining 80 samples as a validation set for model validation, and acquiring the near infrared spectrum data of all the samples; using a standard method to determine the PCTFE content in the samples; subjecting the spectrum data of the validation set samples in the wave bands of 6102.0cm<-1>-5697.0cm<-1> and 4680.2cm<-1>-4242.9cm<-1> to a first order derivative treatment, correlating the treated spectrum data with the PCTFE content by a partial least squares method, and establishing the calibration model by cross validation; employing the calibration model to predetermine the PCTFE content of the validation set samples, and selecting an optimal model according to a minimum root mean squared error of prediction (RMSEP) of the validation set; and acquiring the near infrared spectrum data of the samples to be determined, and making use of the optimal model to obtain the PCTFE content directly. Being suitable for determination of the PCTFE content in a PBX explosive, the method has the characteristics of convenient operation, and rapid and accurate analysis.

The invention provides a method for solving collaborative filtering recommendation data sparsity based on a neural network. The method for solving collaborative filtering recommendation data sparsity based on the neural network adopts generalized regression of neural network (GRNN) and conducts full filling on sparse data by a train network model and score prediction. The method for solving collaborative filtering recommendation data sparsity based on the neural network comprises the following steps: before conducting the GRNN training, conducting screening on input variables of the neural network by adopting mean impact value (MIV), choosing characteristic values having great impact on output as valid input variables; using the valid input variable to construct the input matrix of the GRNN; adopting Kfold cross validation circulation to find out an optimal spread value of the GRNN; using the optimal spread value and the corresponding input matrix and output matrix to conduct GRNN training; using the trained GRNN to conduct score prediction on a sparse score matrix; and replacing non-scored data of the sparse score matrix with predicted score values. The method for solving collaborative filtering recommendation data sparsity based on the neural network can conduct fully filling on sparse recommendation data, solve the data height sparsity problem most outstanding in existing collaborative technology, and enable recommendation result to be accurate.

The invention discloses a shopping mall building air conditioner cooling load prediction method based on GBDT, a storage medium and equipment, and the method comprises: collecting cooling load data, and carrying out the normalization processing to serve as the cooling load energy consumption prediction; establishing a load prediction model based on a gradient lifting decision tree algorithm; inputting the preprocessed data into a prediction model for training, selecting a grid search-cross validation mode, and optimizing the three hyper-parameters with the maximum influence on the performanceof the GBDT model; establishing a final cold load prediction model by completing parameter optimization of the prediction model, and obtaining a predicted cold load curve according to the parameters and the structure of the prediction model; and evaluating the prediction performance of the prediction model, adopting the prediction error for evaluation, enabling the deviation between the true valueand the prediction value to form the prediction error, and completing mall building air conditioner cooling load prediction. The method has good prediction precision, universality and applicability,and is especially suitable for large public buildings with periodically changing cold loads.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are received, which are acquired by a plurality of types of sensors deployed on the vehicle to provide information about surrounding of the vehicle. Based on at least one model, one or more surrounding items are tracked from a first of the plurality of types of sensor data acquired by a first type sensors. At least some of the tracked items are automatically labeled via cross validation and are used to locally adapt, on-the-fly, the at least one model. Model update information is received which from a model update center, which is derived based on the labeled at least some items. The at least one model is updated using the model update information.

The invention discloses a protein-protein interaction site prediction method based on a deep map convolutional network; the method comprises the following steps: extracting a node feature matrix and an adjacent matrix containing side information according to the sequence and structure information of protein, and forming protein map representation together; carrying out deep map convolution based on an initial residual error and identical mapping; inputting the output of the last layer of image convolution layer of the deep image convolution into a multi-layer perceptron, and completing construction of the deep image convolution neural network; extracting training data to obtain protein map representation, and training the deep map convolutional neural network by adopting a five-fold cross validation method; and extracting to-be-detected data to obtain protein map representation, and inputting the protein map representation into the trained deep map convolutional neural network to realize prediction of protein-protein interaction sites. According to the method, protein space structure information can be fully utilized, and the accuracy of protein-protein interaction site prediction is further improved.