7 results about "Learning machine" patented technology

The invention discloses a software defect prediction method and device and electronic equipment, and the method comprises the steps: obtaining a feature vector of to-be-predicted software; and determining a software defect prediction result of the to-be-tested software based on the feature vector and a pre-trained prediction model used for software defect prediction with the prediction model beingobtained based on a gradient lifting algorithm and random forest learning machine training. By adopting the method provided by the invention, the gradient lifting algorithm and the random forest learning machine are used for training to obtain the training model for software defect prediction, so that the software defect prediction result output by the prediction model obtained by training is relatively high in accuracy, and meanwhile, relatively large influence on the calculation complexity is avoided.

The invention provides a data processing method and device based on sentiment analysis, and relates to the technical field of computers. The method comprises the following steps: obtaining an originaltext database, wherein the original text database comprises text data; obtaining a sentiment analysis dictionary; obtaining a first sentiment weight according to the sentiment analysis dictionary; obtaining an online sequence-extreme learning machine model; dynamically adjusting a prediction model according to the online sequence-extreme learning machine model, and obtaining a second sentiment weight; and determining a third sentiment weight according to the first sentiment weight and the second sentiment weight. The data processing method based on the sentiment analysis provided by the invention solves the technical problem that quick and accurate sentiment analysis cannot be carried out on a real-time network data stream in the prior art, and achieves the technical effect of realizing quasi-real time sentiment analysis and processing on massive texts on the basis of a big data platform.

The invention discloses a multi-core clustering method for rapidly processing the missing heterogeneous data. The multi-core clustering method comprises the following steps of 1, carrying out 0 filling initialization on the missing multi-source heterogeneous data; 2, carrying out multi-core learning on the initialized multi-source heterogeneous data by utilizing a plurality of base kernel functions to generate a multi-core matrix; 3, performing multi-core clustering on the generated multi-core matrix to generate a pseudo label; then carrying out the missing value updating on each basic core matrix forming the multi-core matrix by using low-rank estimation; and 4, based on a clustering result, learning the multi-core joint coefficient by using an extreme learning machine. According to the method, the multi-core clustering method is used for achieving the fast learning of the heterogeneous data, the core completion technology is used for fully completing the information of missing data,the clustering performance is improved, and the problem that a traditional multi-core clustering method cannot effectively process the multi-source heterogeneous data is solved.

The invention relates to a relevance feedback method based on a transfinite learning machine. The relevance feedback method comprises the following steps: inputting an inquiry image; retrieving the image to obtain a retrieval result, and marking a result by a user; respectively extracting an SIFT characteristic, a Colour characteristic and an LBP characteristic from the marked image; training three basis classifiers by utilizing the three kinds of characteristics; respectively putting the image in a retrieval image library in the three basis classifiers, voting according to a prediction result, and automatically marking each unmarked image; training and updating the classifiers again; classifying the image in the image library; and returning a result. The relevance feedback method disclosed by the invention is established on the basis of the transfinite learning machine; human-computer interaction is carried out by introducing the inquiry intention of human beings; learning data are enriched by effectively utilizing the unmarked image in the image library; therefore, the image feedback precision is greatly increased; furthermore, the processing speed is controlled well; expression of the image in a computer accords with understanding of human beings to image semantics well; and thus, the relevance feedback method has a good feedback effect.