Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Quantized kernel least mean square (QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. With the purpose of compressing the input to further improve learning performance, this paper proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the data set are compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost.  conduct the intelligent data analysis experiments to test our proposed EQ-KLMS, and the results demonstrate the desirable performance of our scheme.
['Xiong Luo', 'Jing Deng', 'J. Liu', 'Weiping Wang', 'Xiaojuan Ban']
I am writing a research paper on a idea A quantized kernel least mean square scheme with entropy-guided learning for intelligent data analysis
[{"from": "human", "value": "I am writing a research paper on a idea A quantized kernel least mean square scheme with entropy-guided learning for intelligent data analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Quantized kernel least mean square (QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. With the purpose of compressing the input to further improve learning performance, this paper proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the data set are compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost.  conduct the intelligent data analysis experiments to test our proposed EQ-KLMS, and the results demonstrate the desirable performance of our scheme."}]