46results about How to "Reduce classification error" patented technology

The invention relates to a SegNet remote sensing image semantic segmentation method combined with random walk, which is divided into a SegNet initial segmentation step and a random walk optimization segmentation step, the SegNet initial segmentation step is to output the initial semantic segmentation image and category strength information through SegNet; random In the walk optimization segmentation step, first select the random walk seed area, calculate the classification significance index of different categories according to the classification intensity information output by SegNet, and set the threshold to select different types of seed areas; secondly, according to the original image gradient and The classification strength information of SegNet is used to calculate the undirected edge weight; the third step is to start from the seed area and combine the undirected edge weight to perform a random walk on the entire initial segmented image, and finally obtain the optimization on the entire image Split results. The invention walks randomly on the whole image, realizes prediction error and control, greatly reduces edge burrs and patchy classification errors, and completes high-precision semantic segmentation of remote sensing images.

The invention discloses an intrusion detection method based on a two-layer heterogeneous integrated learner, comprising the following steps: using a PKPCA data dimensionality reduction algorithm to perform dimensionality reduction processing on original data to obtain a preprocessing data set; using N classifiers to preprocess The data set is processed, and the hierarchical ten-fold cross-validation method is used to prevent overfitting; the classifier evaluation algorithm is used to select the best M classifiers as heterogeneous learners, where 2≤M