Relevance vector regression incremental learning algorithm and system based on sample characteristics

Abstract

The invention discloses a relevance vector regression incremental learning algorithm and system based on sample characteristics. The method comprises the following steps of: S1, obtaining an initial sample set, and initializing parameters; S2, training the sample set to obtain an RVM prediction model; S3, calculating a sample label, a local density factor and an error factor of each sample; S4, predicting a future sample to be input according to the RVM prediction model; S5, calculating sample characteristic vectors, arranging the sample characteristic vectors in a descending order, performing circulation, if the time of non-relevance vectors is beyond a set threshold value, deleting the sample from the sample set, and exiting the circulation; and S6, judging whether a new input sample exists or not, if so, adding a new sample to form a new sample set, turning to the step S2, and if not, outputting the predicted future sample. By means of the relevance vector regression incremental learning algorithm and system disclosed by the invention, a sample including effective information can be reserved; an invalid sample can be deleted; the prediction precision is relatively high; the time complexity is relatively low; and thus, the relevance vector regression incremental learning algorithm and system can be widely applied in real-time data processing and prediction.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a correlation vector regression incremental learning algorithm and system based on sample characteristics. Background technique [0002] In 2001, Tipping first proposed a new machine learning algorithm called correlation vector machine. Soon, Tipping proposed a fast sequence sparse Bayesian learning algorithm, which accelerated the training speed; then summarized the basic theory and application prospects of correlation vector machines, and published corresponding articles, thus marking the theoretical system of correlation vector machines initial completion. At the same time, the incremental learning method has also achieved a lot of research results at home and abroad, and its application is becoming more and more extensive. Nikolay et al first applied the idea of ​​incremental learning in the correlation vector machine algorithm in 2005. In the research of correlation vector...

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a method with higher prediction accuracy to solve the defects in the prior art that the continuous expansion of the number of samples leads to long training time, and the indiscriminate deletion of samples leads to the loss of effective information of the samples and the lack of full use of the characteristics of the samples themselves. , and the correlation vector regression incremental learning algorithm and system based on sample characteristics with lower time complexity

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