Multiple kernel learning classification method based on noise probability function

A technology of probability function and classification method, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problems of small amount of calculation, noise sensitivity, etc., to achieve small amount of calculation, solve noise-sensitive problems, robustness Good results

Inactive Publication Date: 2017-04-26
UNIV OF ELECTRONICS SCI & TECH OF CHINA +1
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Problems solved by technology

[0006] The purpose of the present invention is to provide a multi-core learning classification method based on the noise probability function in order to solve the above problems. This method does not need to solve complex optimization problems, has a small amount of calculation, and effectively solves the problem of sensitivity to noise.

Method used

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  • Multiple kernel learning classification method based on noise probability function
  • Multiple kernel learning classification method based on noise probability function
  • Multiple kernel learning classification method based on noise probability function

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Experimental program
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Embodiment

[0062] In order to verify the correctness and effectiveness of this method, we use 6 UCI datasets for experiments. For each data set, 8 kernel functions (5 Gaussian kernel functions, 3 polynomial kernel functions) are used, as shown in Table 1 below:

[0063] Table 1 Information of UCI dataset

[0064] Datasets Samples Features Classes Balance-scale 567 4 2 Breast-cancer 569 32 2 Ionosphere 351 34 2 Blood-transfusion 748 5 2 Diabetic-retinopathy 1151 20 2 Pima-indians 768 8 2

[0065] At each noise level, the experiment was repeated 30 times on the data set, and the experimental results were the average of 30 experiments, as shown in Table 2 below:

[0066]

[0067]

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Abstract

The invention discloses a multiple kernel learning classification method based on a noise probability function. The method comprises the following steps: calculation of the noise probability function; selection of a base classifier ft<*>(x) and calculation of corresponding coefficients at<*> in each round of iteration; and update of weight. The multiple kernel learning classification method based on the noise probability function is suitable for a classification algorithm of a noise-polluted data set, and has the advantages of no need to solve the complex optimization problem, being small in calculation amount compared with a conventional multiple kernel learning method, effectively solving the problem of noise sensitivity of conventional multiple kernel boosting learning, and being better in robustness.

Description

technical field [0001] The invention relates to a multi-core learning classification method based on a noise probability function and belongs to the technical field of data mining. Background technique [0002] Linear Support Vector Machine (SVM) was proposed by Cortes and Vapnik. With the deepening of SVM research, SVM has penetrated into many fields of machine learning, such as pattern classification, regression estimation, probability density estimation, etc. SVM has achieved great success, but it belongs to Single Kernel Learning and has certain limitations. [0003] In the field of machine learning, multiple kernel learning (Multiple Kernel Learning) has attracted more and more attention, because compared with single-kernel learning, multi-kernel learning can overcome the huge scale of sample features, heterogeneous information, multi-dimensional data irregularity and data distribution in high-dimensional feature space. uneven phenomenon. [0004] In recent years, a v...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24147
Inventor 武德安冯杰吴磊陈鹏冯江远
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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