A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral analysis and SVM classification, the approach improves ...
Abstract: Support Vector Machines (SVMs) are powerful supervised learning algorithms that are extensively used for both classification and regression tasks. An important component of SVMs is the ...
Abstract: In many different fields, Support Vector Machines (SVMs) have shown to be an effective tool for regression and classification problems. When using support vector machines (SVMs), the kernel ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results