An Online Soft Margin Kernel Learning Algorithm Based on Step Size Control

A soft interval and kernel learning technology, applied in the field of online soft interval kernel learning algorithms, can solve the problem that classification methods cannot efficiently handle data stream classification, online learning algorithms cannot suppress noise effects, etc., to improve classification accuracy and reduce computational complexity. degree, update smooth effect

Active Publication Date: 2022-03-08
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

[0003] The purpose of the present invention is to propose an online soft interval kernel learning algorithm based on step size control, aiming at the fact that the existing classification method based on batch processing technology cannot efficiently handle the data stream classification problem, and the online learning algorithm cannot suppress the influence of noise. It can reduce the storage space of the model, effectively control the influence of noise, significantly improve the efficiency of model update, and meet the real-time requirements of practical application problems

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  • An Online Soft Margin Kernel Learning Algorithm Based on Step Size Control
  • An Online Soft Margin Kernel Learning Algorithm Based on Step Size Control
  • An Online Soft Margin Kernel Learning Algorithm Based on Step Size Control

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Embodiment 1

[0033] Embodiment 1: Take the online classification experiment on the original benchmark data sets ijcnn, codrna, and eegeye as an example for illustration. Such as figure 1 Shown is a schematic diagram of an online soft interval kernel learning algorithm based on step size control provided according to an embodiment of the present invention. The online learning algorithm includes the following steps:

[0034] Step 1: Initialize model parameters, decision function and model kernel function. The specific steps are:

[0035] Initialize the model threshold parameter C=0.05, initialize the decision function f of the binary classification problem 0 =0, specify the Gaussian kernel function as the model kernel function, namely k(x i ,x j )=exp(-‖x i -x j ‖ 2 / d), where d is taken as the dimensionality of the sample input x.

[0036] Step 2: collect the data stream, and use the classification decision function to predict the category label of the data stream sample. The speci...

Embodiment 2

[0046] Embodiment 2: On the basis of the original benchmark data sets ijcnn, codrna, and eegeye, noise labels are added, and an online classifier is trained on the data sets containing noise labels. The difference from Embodiment 1 is that in this embodiment, in Step 1, 30% of the data set is randomly selected as a test set, and the rest of the data is added to noise labels to construct a training set. Specifically, we respectively modulo 20, modulo 10, and modulo 5 the sample indices, and multiply the sample point labels with a remainder of 0 by -1 to obtain the noise label data.

[0047] Figure 3-5 For training online classifiers KernelPerceptron, Pegasos and OSKL on datasets ijcnn, codrna and eegeye with noisy labels, and the average classification performance (average test accuracy, ACA) on the original 30% dataset without noise test dataset. The experimental results show that as the noise of the training samples indexed by mod20, mod10 and mod5 increases, the classifica...

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Abstract

The invention relates to an online soft-margin kernel learning algorithm (OSKL) based on step size control. A nonlinear classifier is constructed by introducing a kernel function, a soft interval parameter is introduced to control the influence of noisy data, and a robust online kernel learning algorithm is designed based on the basic framework of the online gradient descent algorithm. The algorithm can reduce the storage space of the model, effectively control the influence of noise, and the computational complexity of model update is only O(1). It has the advantages of strong real-time performance and easy implementation. It is a natural tool for processing and analyzing data flow problems. The online learning algorithm of the present invention overcomes the problem that the traditional classification method based on batch processing technology cannot efficiently process data streams, and also overcomes the problem that existing online learning algorithms such as Kernel Perceptron and Pegasos cannot effectively suppress the influence of noise.

Description

technical field [0001] The invention belongs to the field of data mining and machine learning, and relates to data mining and data processing methods, in particular to an online soft interval kernel learning algorithm (OSKL) based on step size control. Background technique [0002] Classification problem is a classic research problem in the field of data mining and machine learning. The traditional classification method based on batch processing technology first collects data, builds a learning model based on the collected data, and selects an optimization algorithm to solve the model to obtain a classifier. With the rapid development of e-commerce, social media, mobile Internet, Internet of Things and other technologies, more and more application scenarios need to process large-scale data streams in real time. Traditional classification methods based on batch processing technology have many shortcomings such as high computational complexity and low model update efficiency ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N20/10G06K9/62
CPCG06F18/214G06F18/24
Inventor 宋允全李月菱于琪雷鹤杰梁锡军渐令
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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