Digital media object classification method based on large margin distributed learning

Abstract

The invention discloses a digital media object classification method based on large margin distributed learning and aims to solve the problem that marking of classes of digital media causes noise. The form of the classification problem of the digital media objects is finally reduced into a convex secondary optimization problem by maximizing a margin average and minimizing a margin variance; two optimization algorithms which are based on dual coordinate descent and average stochastic gradient descent respectively are achieved according to whether or not to use a nonlinear kernel function and according to features of a training digital media object base; users can make selections on their own according to actual conditions; if the users select the nonlinear kernel function, the DCD (dual coordinate descent) is selected as the optimization algorithm for training; if the users select a linear kernel function, the training digital media object base provide many samples or has few features, the ASGD (average stochastic gradient descent) is selected as the optimization algorithm for training, and if not, the DCD is still selected as the optimization algorithm.

Description

technical field [0001] The invention relates to a digital media object classification method, in particular to a digital media object classification method based on large interval distribution learning. Background technique [0002] The current human society has entered the stage of digitalization in an all-round way. Currently, images, texts, videos, audios and other media used to disseminate information are recorded and processed in the form of binary codes. These encoded images, texts, videos, audios Collectively referred to as Digital Media Objects. Digital media objects have been widely used in all walks of life, such as remote sensing measurement and control, Internet sites, digital TV, telephone communications, etc. These industries accumulate a large amount of data every day, so as the amount of data continues to expand, how to effectively organize and manage digital media objects becomes more and more important, and the core issue is the classification of digital m...

AI Technical Summary

Problems solved by technology

Traditional classification methods based on large intervals, such as support vector machines (hereinafter abbreviated as SVM), are sensitive to noise because they only consider the interval of a single sample, and are not suitable for directly classifying digital media objects.

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