Personalized Intelligent Recommendation of English Vocabulary Learning Based on Weight Kernel Learning-Support Vector Regression

The essential of vocabulary knowledge as one of the most difficult aspects of learning a foreign language is widely recognized. However, there has been a lack to analysis on English vocabulary that concentrated on authority of content and quality of Wikipedia data, such as content authority, which affect classification accuracy. This paper, proposed Weight Multiple Kernel Learning -Support Vector Regression (WMK-SVR) technique is performed efficiently in classification for enhance the accuracy. The WMK-SVR are used to transform input data into a higher dimensional space where linear regression and capture different aspects of the data, enhancing the model learning capability. wiki dataset and pre-processing involved Square Root Cubature Kalman Filter (SRCKF) is enable to cleaning the data from the document words in the English text. Feature extraction using Local Maximum Synchro Squeezing Transform (LMST) is extract the feature and offers better frequency resolution compared to conventional wavelet transforms. The proposed WMK-SVR method is evaluated using the Wikipedia data achieving a higher accuracy of 91.25% respectively. The existing techniques such as Neural Machine Translation (NMT)and Natural Language Processing (NLP) are evaluated the proposed method.