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40 results about "Local selection" patented technology

Hierarchical categorization method and system with automatic local selection of classifiers

The present invention relates generally to the classification of items into categories, and more generally, to the automatic selection of different classifiers at different places within a hierarchy of categories. An exemplary hierarchical categorization method uses a hybrid of different classification technologies, with training-data based machine-learning classifiers preferably being used in those portions of the hierarchy above a dynamically defined boundary in which adequate training data is available, and with a-priori classification rules not requiring any such training-data being used below that boundary, thereby providing a novel hybrid categorization technology that is capable of leveraging the strengths of its components. In particular, it enables the use of human-authored rules in those finely divided portions towards the bottom of the hierarchy involving relatively close decisions for which it is not practical to create in advance sufficient training data to ensure accurate classification by known machine-learning algorithms, while still facilitating eventual change-over within the hierarchy to machine learning algorithms as sufficient training data becomes available to ensure acceptable performance in a particular sub-portion of the hierarchy.
Owner:HEWLETT-PACKARD ENTERPRISE DEV LP

Hierarchical categorization method and system with automatic local selection of classifiers

The present invention relates generally to the classification of items into categories, and more generally, to the automatic selection of different classifiers at different places within a hierarchy of categories. An exemplary hierarchical categorization method uses a hybrid of different classification technologies, with training-data based machine-learning classifiers preferably being used in those portions of the hierarchy above a dynamically defined boundary in which adequate training data is available, and with a-priori classification rules not requiring any such training-data being used below that boundary, thereby providing a novel hybrid categorization technology that is capable of leveraging the strengths of its components. In particular, it enables the use of human-authored rules in those finely divided portions towards the bottom of the hierarchy involving relatively close decisions for which it is not practical to create in advance sufficient training data to ensure accurate classification by known machine-learning algorithms, while still facilitating eventual change-over within the hierarchy to machine learning algorithms as sufficient training data becomes available to ensure acceptable performance in a particular sub-portion of the hierarchy.
Owner:HEWLETT-PACKARD ENTERPRISE DEV LP

Nonconvex compressed sensing image reconstruction method based on local similarity and local selection

The invention discloses a nonconvex compressed sensing image reconstruction method based on local similarity and local selection. The method comprises the following steps: 1) carrying out observation and reception after an image is partitioned; 2)utilizing a local growth method to carry out clustering on observation vectors of all the image blocks; 3) carrying out population initialization on the image block corresponding to each kind of observation vector according to the scheme that the polyatom direction and monatom direction coexist; 4) utilizing an improved genetic algorithm to carry out crossing, variation and selection operation based on a local selection mechanism on the populations obtained in the step 3), reconstituting corresponding image blocks and obtaining optimal atom combinations; 5) utilizing a clone selection optimization algorithm to study the optimal atom combinations on the aspects of dimension and displacement; and 6) piecing the image blocks obtained in the step 5) together in sequence to obtain a complete reconstructed image, and outputting the complete reconstructed image. The reconstructed image is good in visual effect and high in peak signal to noise ratio, and can be used for nonconvex compressed sensing reconstruction of image signals under the condition of low sampling rate.
Owner:XIDIAN UNIV

A wind speed prediction method based on a depth limit learning machine and a system and a unit thereof

The invention discloses a wind speed prediction method based on a depth limit learning machine, a system and a generator set thereof, belonging to the field of wind turbine generators. The wind speedvalue is predicted by the time series prediction method of the depth limit learning machine, which comprises the following steps: acquiring a group of historical observation data series at time t andbefore time t in the generation process of the wind turbine; extracting the training sample data set by the time series prediction method of depth limit learning machine, performing derivation of theprediction model: the nearest neighbor of the prediction sequence Q is extracted from the training sample data set as the recombination sample, when the recombination sample corresponds to single-stepprediction, the multi-step prediction has the single-step feature and the multi-step feature, training the DELM models with single-step feature and multi-step feature and multi-hidden layer by depthlimit learning machine, and integrating into a prediction model through local selection, and predicting the wind speed value Xt + s by the prediction model. The method of the invention has high accuracy and generalization performance, and can improve prediction performance and real-time update capability.
Owner:GUODIAN UNITED POWER TECH

A Nonconvex Compressive Sensing Image Reconstruction Method Based on Local Similarity and Local Selection

The invention discloses a nonconvex compressed sensing image reconstruction method based on local similarity and local selection. The method comprises the following steps: 1) carrying out observation and reception after an image is partitioned; 2)utilizing a local growth method to carry out clustering on observation vectors of all the image blocks; 3) carrying out population initialization on the image block corresponding to each kind of observation vector according to the scheme that the polyatom direction and monatom direction coexist; 4) utilizing an improved genetic algorithm to carry out crossing, variation and selection operation based on a local selection mechanism on the populations obtained in the step 3), reconstituting corresponding image blocks and obtaining optimal atom combinations; 5) utilizing a clone selection optimization algorithm to study the optimal atom combinations on the aspects of dimension and displacement; and 6) piecing the image blocks obtained in the step 5) together in sequence to obtain a complete reconstructed image, and outputting the complete reconstructed image. The reconstructed image is good in visual effect and high in peak signal to noise ratio, and can be used for nonconvex compressed sensing reconstruction of image signals under the condition of low sampling rate.
Owner:XIDIAN UNIV
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