33results about How to "Low classification error rate" patented technology

A method and apparatus for performing discriminative training of, for example, call routing training data (or, alternatively, other classification training data) which improves the subsequent classification of a user's natural language based requests. An initial scoring matrix is generated based on the training data and then the scoring matrix is adjusted so as to improve the discrimination between competing classes (e.g., destinations). In accordance with one illustrative embodiment of the present invention a Generalized Probabilistic Descent (GPD) algorithm may be advantageously employed to provide the improved discrimination. More specifically, the present invention provides a method and apparatus comprising steps or means for generating an initial scoring matrix comprising a numerical value for each of a set of n classes in association with each of a set of m features, the initial scoring matrix based on a set of training data and, for each element of said set of training data, based on a subset of said features which are comprised in the natural language text of said element of said set of training data and on one of said classes which has been identified therefor; and based on the initial scoring matrix and the set of training data, generating a discriminatively trained scoring matrix for use by said classification system by adjusting one or more of said numerical values such that a greater degree of discrimination exists between competing ones of said classes when said classification requests are performed, thereby resulting in a reduced classification error rate.

The invention relates to a deep convolutional neural network method based on information lossless pooling. The method is used for image classification, and comprises the steps of collecting different kinds of images and marking image categories as image label information; performing image set division, and dividing the collected images into a training set, a verification set and a test set; designing a convolutional neural network structure based on information lossless pooling, including the number of convolutional layers and the number of information lossless pooling layers that are used, designing the number of filters in the convolutional layers, designing Gaussian smoothing filter parameters of the information lossless pooling layers, the pooling window size and a convolutional filter structure for feature fusion, designing the number of network training loop iterations and network final convergence conditions, and initializing network parameters; and inputting training data to the network in batches for calculation and training.

The invention belongs to the technical field of intelligent automobile running environment perception, and discloses an automobile running working condition judgment system and a judgment method of the automobile running working condition judgment system. The method comprises the steps that the parameters of the speed and pedal opening degree of an automobile are selected to be treated, character parameters in speed signals and pedal signals representing the working condition are extracted, the character parameters are firstly reduced for the first time through a correlational analysis method, the first fifteen parameters with largest correlation and the working condition are selected for correlation analysis, ten character parameters are selected from large to small according to the correlation as the standard for dividing the working condition, then the ten character parameters are reduced for the second time through kernel principal component analysis, character parameter values are reduced to seven, and finally the running working condition of the automobile is classified based on the semi-supervised nucleus fuzzy C-mean value clustering analysis of an objective function. According to the system and method, the judgment speed is high and accurately, the fuel consumption and tail gas emissions in the automobile running process can be reduced through the judgment result, and the system and method have great significance in the study of various new automobile type development, dynamic property matching of the whole automobile and the like.

A method for classifying enterprise domains and screening enterprise keywords is disclosed by the invention. The method obtains the feature words of the related fields through training the enterprisedomain corpus as a classification dictionary, and then uses the classification dictionary to classify the enterprise profile documents. After classification, the method can also extract the industry label representing the enterprise domain from the enterprise profile documents. In addition, the method can overcome the influence of most noisy words in Chinese text processing, and has low error rate, high classification efficiency and excellent comprehensive performance.

The invention discloses an artificial immunization non-supervision image classification method based on manifold distance and relates to the technical field of image processing. The specific process includes: (1) inputting an image to be classified and setting an initialization parameter to generate initialized antibody population; (2) based on the manifold distance, classifying the category of the sample point of the image to be classified and calculating the affinity of the antibody population; (3) carrying out clonal proliferation operation on the antibody population; (4) carrying out clonal variation operation on the antibody population after clonal proliferation; (5) classifying the category of the image to be classified according to a code of the antibody population after clonal variation and calculating the affinity of the antibody population; (6) carrying out clonal selection operation on the antibody population according to the antibody affinity; and (7) according to set maximum iterations, judging the stop condition of the category classification result of the image to be classified and determining the final classification result. The classification method has the advantages of low sensitivity of image data structure, non-supervision execution, good classification effect and strong robustness, and can be applied to the target identification in the field of image processing.

The invention discloses a 3D shape image classification method of an isovariant 3D convolutional network model based on a partial differential operator, and the method comprises the steps of: carrying out the parametric modeling of a convolution kernel by employing the partial differential operator, solving a 3D rotating group and the feature domain of each convolution layer, obtaining an isovariant convolution kernel, and building an isovariant 3D convolutional network model PDO-e3DCNN, wherein the input of the PDO-e3DCNN is a 3D shape, and the output of the PDO-e3DCNN is prediction classification of the 3D shape, and the PDO-e3DCNN is used for 3D shape classification and identification visual analysis. According to the method, picture data with direction features can be effectively processed, and a lower 3D shape image classification error rate can be achieved on a data set by using fewer parameters.

The invention provides a fast structural SVM text classification optimization algorithm. For a text classification task of an unbalanced data set, the algorithm directly optimizes the performance evaluation index of the main category by using the performance evaluation method such as on an accuracy rate, a recall rate, an AUC, and the like. The method is different from most conventional text classification algorithms that: instead of learning a single rule to predict a tag of a single sample, the method formalizes the learning problem into a multiple predictive problem on all the samples in the data set, and is different from idea in the conventional method that the reduction of overall classification error rate is taken as the target, so that classification accuracy of the text data set under the unbalanced condition is improved, and the classification performance is effectively improved; and referring to a Structural SVM based sparse approximation algorithm, with better time complexity, the method can be used for evaluation indexes such as the F value calculated from the accuracy rate and the recall rate, and the optimization of the AUC, so that the time complexity is reduced and better results are obtained.

The invention provides an intelligent evaluation method for satellite remote sensing image availability, comprising the following steps: S1, inputting images; S2, according to the user requirements, inputting the surface feature type and calculating the surface feature influence parameters of the surface feature type; S3, performing cloud detection on the image inputted in the step S1; 4, calculating the cloud thickness, the cloud fragmentation degree and the cloud coverage rate according to the cloud detection result; S5, inputting the cloud thickness, the cloud fragmentation degree and the cloud coverage calculated in the step S4 into a remote sensing image availability evaluation model to calculate the objective availability of the corresponding remote sensing image; S6, calculating thefinal availability level of the remote sensing image according to the objective availability calculated in the step S5 and the surface object influence parameter calculated in the step S2; S7: outputting the corresponding remote sensing image according to the final availability level. The invention provides an intelligent evaluation method of remote sensing image availability, which combines theobjective factors influencing the availability and the subjective influence of the related user requirements, and is used for realizing the image availability evaluation through a reasonable step design.

The invention discloses a silent living body detection method. The silent living body detection method sequentially comprises the following steps: 1, preprocessing operation is performed on an input video to obtain a face picture with a fixed size; 2, the face image with the fixed size obtained in the step 1 is represented by RGB and YCrCb color spaces, and then the face image is input into a double-flow feature fusion network model to achieve feature extraction; and step 3, a non-living body category is subdivided into a printing attack category and a display attack category, category prediction is performed according to the image feature representation obtained in the step 2, and a multi-classification cross entropy loss supervision model is adopted for training, so that the classification error rate of the algorithm can be effectively reduced, and the generalization performance can be improved. According to the invention, single-frame picture input is adopted, additional auxiliary equipment is not needed, the algorithm implementation cost is low, additional man-machine interaction is not needed, and good user experience is achieved.

The invention discloses a method for detecting the edge of a regularized straight line section of an image of a texture-free metal part. According to the method, the gradient of each pixel point of the input part image is calculated firstly, then a preliminary straight line segment is detected by using an LSD algorithm, and discontinuous straight line segments are connected by using the distance and angle relationship between the straight line segments in the next step, so that the purpose of detecting complete straight line segments is achieved, and the problem that the detected straight linesegments are broken is avoided. The method is improved on the basis of an LSD algorithm, can output a complete straight line segment on the basis of keeping the high speed of the LSD algorithm, is suitable for an RGB image and a gray level image, and can meet the requirements of practical application.

The invention relates to the technical field of deep residual networks, and discloses a regional water resource classification evaluation method based on an improved deep residual network, which comprises four steps of image enhancement, image fusion, image marking and image segmentation. According to the regional water resource classification evaluation method based on the improved deep residual network, image enhancement is used for improving image information and quality, so that the features of the image are more obvious under human eye observation, the regional water resource identification effect is enhanced, visual analysis ignores the digital interference degree, and the method plays an important role in various aspects of remote sensing images; according to image fusion, a multi-spectral image with low spatial resolution and a single-band image with high spatial resolution are replayed and sampled to generate a new multi-spectral image with high resolution, so that the generated image has high spatial resolution and multi-spectral characteristics, a composite mode is mainly adopted, information provided by remote sensing image data sources of different sensors is integrated, and multi-spectral image fusion is realized. And high-quality image information is obtained.

The invention relates to a tool wear state evaluation method based on optimal characteristics and lion group optimization SVM. The method comprises the following steps: S1, data preprocessing; s2, feature extraction; s3, performing feature filtering to obtain an optimal feature subset with the highest correlation and the minimum redundancy; s4, constructing a tool wear evaluation model; and S5, evaluating the wear state of the cutter. The feature subset obtained through the method is higher in relevancy and minimum in redundancy, the SVM classifier optimized through the lion group algorithm is low in time complexity, higher in optimization capacity, faster in iteration and lower in classification error rate, the recognition rate reaches 97.125%, the tool wear state evaluation speed and stability can be improved, the production efficiency is improved, and the production cost is reduced.